International Conference on Network Science and Graph Analytics

Introduction of the conferences

The International Conference on Network Science and Graph Analytics (NSGA) is an annual event that brings together researchers, practitioners, and industry experts from around the world to share their latest research and developments in the fields of network science and graph analytics. The conference aims to provide a platform for discussions on the latest trends and innovations in the field, as well as foster collaborations and networking opportunities among attendees.The conference covers a wide range of topics related to network science and graph analytics, including network modeling, analysis and visualization, social networks, big data analytics, machine learning, and data mining. The conference also features keynote speakers, workshops, tutorials, and panel discussions on various topics related to network science and graph analytics. NSGA welcomes submissions from researchers and practitioners in academia, industry, and government organizations. All submitted papers are peer-reviewed and the accepted papers are published in the conference proceedings. NSGA is a highly respected conference in the field of network science and graph analytics, attracting participants from all over the world. It provides an excellent opportunity for researchers and practitioners to exchange ideas, share their work, and learn about the latest developments in the field.

Theme: The theme of the International Conference on Network Science and Graph Analytics can vary depending on the specific conference in question.

Theme

The theme of the International Conference on Network Science and Graph Analytics can vary depending on the specific conference in question.

Objectives

The objectives of the International Conference on Network Science and Graph Analytics can vary, but some common objectives include:

1. To provide a platform for researchers, scientists, engineers, and industry professionals to exchange ideas and knowledge on the latest developments in the field of Network Science and Graph Analytics.
2. To promote interdisciplinary collaboration and encourage cross-fertilization of ideas across different fields and industries, including material science, physics, chemistry, biology, and engineering.
3. To showcase the latest research findings and technological advancements in the field of Network Science and Graph Analytics, and to provide an opportunity for researchers to present their work to a global audience.
4. To facilitate the transfer of knowledge and technology from academia to industry and to encourage the commercialization of new Network Science and Graph Analytics-based products.
5. To foster the development of new partnerships and collaborations between researchers, industry, and government, with the goal of promoting the growth and development of the field of Network Science and Graph Analytics.
6. To provide a forum for discussing the ethical, social, and environmental implications of Network Science and Graph Analytics and to encourage responsible and sustainable research and development in the field.

These objectives provide a framework for the International Conference on Network Science and Graph Analytics and are designed to advance the field by promoting collaboration, exchange of ideas, and the dissemination of new knowledge and technology.

Organizers

Science Father is a international conferences  organizer and publish the videos, books and news in various themes of scientific research. Articles Presented in our conference are Peer Reviewed. We build the perfect environment for learning, sharing, networking and Awarding via Academic conferences, workshops, symposiums, seminars, awards and other events. We establish our Relationship with the scholars and the Universities through various activities such as seminars, workshops, conferences and Symposia. We are a decisive, conclusive & fast-moving company open to new ideas and ingenious publishing. We also preserve the long-term relationships with our authors and supporting them throughout their careers. We acquire, develop and distribute knowledge by disseminating scholarly and professional materials around the world. All  conference and award presentations are maintain the highest standards of quality, with Editorial Boards composed of scholars & Experts from around the world.

List of Committee Members

Call for Abstract/paper

Original Articles/papers are invited from Industry Persons, Scientist, Academician, Research Scholars, P.G. & U.G. Students for presentation in our International Conference. All articles/papers must be in MS-Word (.doc or .docx) format, including the title, author's name, an affiliation of all authors, e-mail, abstract, keywords, Conclusion, Acknowledgment, and References.

Submit Abstract

The Candidates with eligibility can click the "Submit Paper/Abstract Now" button and fill up the online submission form and Submit.

Abstract/Full Paper submission

Final/Full Paper submission is optional: If you don't want your abstract/full paper to be published in the Conference Abstracts & Proceedings CD (with ISBN number) and only want to present it at the conference, it is acceptable.

Page limit: There is a limit of 6-8 pages for a final/full paper. An additional page is chargeable.

Paper language: Final/Full papers should be in English.

Templates: "Final paper template," "Final abstract template"

All the final papers should be uploaded to the website online system according to "The final paper template" as word doc. Or Docx, since this will be the camera-ready published version. Please note that final papers that are not uploaded to online System as a word doc./docx after the opening of final paper submissions according to the template above will not be published in the CONFERENCE Abstracts & Proceedings CD (with ISBN)

Journal Publication

Network Science and Graph Analytics Conferences All accepted papers will be included in the conference proceedings, which will be recommended in one of the author's prescribed ScienceFather International journals.

Registration Procedure

• Click the “Register Now” button on the conference page and enter your Submission ID in the Search Box
• Your Submissions will be listed on that page. You can find the Register Now link beside your submission. Click the link, and now you will be redirected to the Conference registration form where you can make your registration using credit/debit cards.
• The Fee charged for E-Poster is to display the E-Posters only on the Website. The Abstract will be published in the conference proceeding book.

Registration Types

Speaker Registration

• Access to all event Session
• Certificate of Presentation
• Handbook
• Conference Kit
• Tea, Coffee & Snack,
• Lunch during the Conference
• Publication of Abstract /Full Paper at the Conference Proceedings Book
• Opportunity to give a Keynote/ Poster Presentations/ Plenary/ Workshop
• Opportunity to publish your Abstract in any of our esteemed Journals discounted rate
• Opportunity to publish your full article in our open access book at a discounted rate
• One to One Expert Forums

Delegate (Participant) Registration

• Access to all Event Sessions
• Participation Certificate
• Handbook
• Conference Kit
• Tea, Coffee & Snack,
• Lunch during the Conference
• Delegates are not allowed to present

Poster Registration

• Includes all the above Registration Benefits
• You will have to bring your Posters to the Conference Venue
• Best poster award memento and certificate on stage.

Poster Guidelines

• The poster should be 1×1 m Size.
• The title, contents, text, and the author’s information should be visible.
• Present numerical data in the form of graphs rather than tables.
• Figures make trends in the data much more evident.
• Avoid submitting high word-count posters.
• Poster contains, e.g., Introduction, Methods, Results, Discussion, Conclusions, and Literature.

Research Forum (Awards)

• Includes all the above Registration Benefits.
• The attendee should be required age limit.
• Award memento and certificate on stage.

E-Poster Presentation

• The amount charged for E-Posters is to display the E-Posters only on the website
• The presenter will get an e-poster participation certificate as a soft copy
• The abstract will be published in the particular journal and also in the conference proceeding book
• The presenter is not required to be present in person at the Conference

Video Presentation

• The amount charged for Video Presentation is to display the Presentation at the Conference.
• The presenter will get Video participation certificate as a soft copy
• The abstract will be published in the particular journal and also in the conference proceeding book
• The presenter is not required to be present in person at the Conference

Accompanying Person

• Accompanying Persons attend the participants at the Conference who may be either a spouse/family partner or a son/daughter and must register under this category.
• Please note that business partners do not qualify as Accompanying Persons and cannot register as an Accompanying Person.

Details of Conference Awards

Sciencefather awards Researchers and Research organizations around the world with the motive of Encouraging and Honoring them for their Significant contributions & Achievements for the Advancement in their field of expertise. Researchers and scholars of all nationalities are eligible to receive Sciencefather Research awards. Nominees are judged on past accomplishments, research excellence, and outstanding academic achievements.

Award Categories

Best Poster Award

Posters will be evaluated based on Presentation Style, Research Quality, and Layout/Design. Unique opportunity to combine visual and oral explanations of your projects in the form of poster presentation. Posters should have the Title (with authors affiliation & contact details), Introduction, Methods, Results (with tables, graphs, pictures), Discussion, Conclusion, References, and Acknowledgements. The size of the poster should be: 1mX1.5m; Text:16-26 pt; Headings: 32-50 pt; Title: 70 pt; Color: Preferable. Bring your poster to the meeting, using tubular packaging and presenting duration: 10 min discussion & 5 min query per person. Eligibility: The presenter can nominate the Award. He must be under 40 years of age as on the conference date.

Best Presentation Award

The presentation will be evaluated based on Presentation Style, Research Quality, and Layout/Design. Unique opportunity to combine visual and oral explanations of your projects in the form of poster presentations. The presentation should have the Title (with authors affiliation & contact details), Introduction, Methods, Results (with tables, graphs, pictures), Discussion, Conclusion, References, and Acknowledgements. Bring your presentation to the meeting, using a pen drive, presenting duration: 10-20 min discussion & 5 min query per person. Eligibility: The presenter can nominate the Award. He must be under 55 years of age as of the conference date.

Best Paper Award

Paper will be evaluated based on Format, Research Quality, and Layout/Design. The paper should have the Title (with authors affiliation & contact details), Introduction, Methods, Results (with tables, graphs, pictures), Discussion, Conclusion, References, and Acknowledgements. Eligibility: The presenter can nominate the Award. He must be under 55 years of age as of the conference date.

Instructions for submission

If you want to submit only your Abstract

• If you want to publish only your abstract (it is also optional) in the CONFERENCE Abstracts & Proceedings CD (with ISBN), upload your abstract again according to the Final abstract template as a word doc. Or Docx.
• If you also don't want your abstract to be published in the CONFERENCE Abstracts & Proceedings CD (with an ISBN) and only want to present it at the conference, it is also acceptable.

How to Submit your Abstract / Full Paper

Please read the instructions below then submit your Abstract/ Full Paper (or just final abstract) via the online conference system:

• STEP 1: Please download the Abstract /Final Paper Template and submit your final paper strictly according to the template: Network Science and Graph Analytics Final Paper Template in word format (.doc /.docx). See a Final abstract template formatted according to the template.
• STEP 2: Please ensure that the Abstract/ full paper follows exactly the format and template described in the final paper template document below since this will be the camera-ready published version. All last articles should be written only in English and "word document" as .doc or .docx.
• STEP 3: You can submit your final paper(s) to the online conference system only by uploading/ Re-submission your current submission.
• STEP 4: After logging/using submission ID in the online conference system, click on the "Re-submission" link at the bottom of the page.
• STEP 5: After the "Re submission page" opens, upload your abstract/ final paper (it should be MS word document -doc. or Docx-).

General Information

• Dress Code: Participants have to wear a formal dress. There are no restrictions on color or design. The audience attending only the ceremony can wear clothing of their own choice.
• Certificate Distribution: Each presenter's name will be called & asked to collect their certificate on the Stage with an official photographer to capture the moments.

ScienceFather Terms & Conditions

Network Science and Graph Analytics Conferences Terms & Conditions Policy was last updated on June 25, 2022.

Privacy Policy

Network Science and Graph Analytics conferences customer personal information for our legitimate business purposes, process and respond to inquiries, and provide our services, to manage our relationship with editors, authors, institutional clients, service providers, and other business contacts, to market our services and subscription management. We do not sell, rent/ trade your personal information to third parties.

Relationship

Network Science and Graph Analytics Conferences Operates a Customer Association Management and email list program, which we use to inform customers and other contacts about our services, including our publications and events. Such marketing messages may contain tracking technologies to track subscriber activity relating to engagement, demographics, and other data and build subscriber profiles.

Disclaimer

All editorial matter published on this website represents the authors' opinions and not necessarily those of the Publisher with the publications. Statements and opinions expressed do not represent the official policies of the relevant Associations unless so stated. Every effort has been made to ensure the accuracy of the material that appears on this website. Please ignore, however, that some errors may occur.

Responsibility

Delegates are personally responsible for their belongings at the venue. The Organizers will not be held accountable for any stolen or missing items belonging to Delegates, Speakers, or Attendees; due to any reason whatsoever.

Insurance

Network Science and Graph Analytics conferences Registration fees do not include insurance of any kind.

Press and Media

Press permission must be obtained from the Network Science and Graph Analytics Organizing Committee before the event. The press will not quote speakers or delegates unless they have obtained their approval in writing. This conference is not associated with any commercial meeting company.

Transportation

Network Science and Graph Analytics Conferences Please note that any (or) all traffic and parking is the registrant's responsibility.

Requesting an Invitation Letter

Network Science and Graph Analytics Conferences For security purposes, the invitation letter will be sent only to those who had registered for the conference. Once your registration is complete, please contact networkquery@researchw.com to request a personalized letter of invitation.

Cancellation Policy

If Network Science and Graph Analytics Conferences cancels this event, you will receive a credit for 100% of the registration fee paid. You may use this credit for another Network Science and Graph Analytics Conferences event, which must occur within one year from the cancellation date.

Postponement Policy

Suppose Network Science and Graph Analytics Conferences postpones an event for any reason and you are unable or indisposed to attend on rescheduled dates. In that case, you will receive a credit for 100% of the registration fee paid. You may use this credit for another Network Science and Graph Analytics Conferences, which must occur within one year from the date of postponement.

Transfer of registration

Network Science and Graph Analytics Conferences All fully paid registrations are transferable to other persons from the same organization if the registered person is unable to attend the event. The registered person must make transfers in writing to networkquery@researchw.com. Details must include the full name of an alternative person, their title, contact phone number, and email address. All other registration details will be assigned to the new person unless otherwise specified. Registration can be transferred to one conference to another conference of ScienceFather if the person cannot attend one of the meetings. However, Registration cannot be transferred if it will be intimated within 14 days of the particular conference. The transferred registrations will not be eligible for Refund.

Visa Information

Network Science and Graph Analytics Conferences Keeping given increased security measures, we would like to request all the participants to apply for Visa as soon as possible. ScienceFather will not directly contact embassies and consulates on behalf of visa applicants. All delegates or invitees should apply for Business Visa only. Important note for failed visa applications: Visa issues cannot come under the consideration of the cancellation policy of ScienceFather, including the inability to obtain a visa.

Refund Policy

Network Science and Graph Analytics Conferences Regarding refunds, all bank charges will be for the registrant's account. All cancellations or modifications of registration must make in writing to networkquery@researchw.com

If the registrant is unable to attend and is not in a position to transfer his/her participation to another person or event, then the following refund arrangements apply:

Keeping given advance payments towards Venue, Printing, Shipping, Hotels and other overheads, we had to keep Refund Policy is as following conditions,

• Before 60 days of the Conference: Eligible for Full Refund less $100 Service Fee
• Within 60-30 days of Conference: Eligible for 50% of payment Refund
• Within 30 days of Conference: Not eligible for Refund
• E-Poster Payments will not be refunded.

Accommodation Cancellation Policy

Network Science and Graph Analytics Conferences Accommodation Providers such as hotels have their cancellation policies, and they generally apply when cancellations are made less than 30 days before arrival. Please contact us as soon as possible if you wish to cancel or amend your accommodation. ScienceFather will advise your accommodation provider's cancellation policy before withdrawing or changing your booking to ensure you are fully aware of any non-refundable deposits.

Our Authorization Policy

By registering for the event, award and conference, you grant ScienceFather permission to photograph, film, record, and use your name, likeness, image, voice, and comments. These materials may be published, reproduced, exhibited, distributed, broadcasted, edited, and/or digitized in publications, advertising materials, or any other form worldwide without compensation. Please note that the taking of photographs and/or videotaping during any session is prohibited. If you have any queries, please feel free to contact us.

Sponsorship Details

Network Science and Graph Analytics Conferences warmly invite you to sponsor or exhibit of International Conference. We expect participants more than 200 numbers for our International conference will provide an opportunity to hear and meet/ads to Researchers, Practitioners, and Business Professionals to share expertise, foster collaborations, and assess rising innovations across the world in the core area of mechanical engineering.

Diamond Sponsorship

1. Acknowledgment during the opening of the conference
2. Complimentary Booth of size 10 meters square
3. Four (4) delegate’s complimentary registrations with lunch
4. Include marketing document in the delegate pack
5. Logo on Conference website, Banners, Backdrop, and conference proceedings
6. One exhibition stand (1×1 meters) for the conference
7. One full cover page size ad in conference proceedings
8. Opportunities for Short speech at events
9. Option to sponsors conference kit
10. Opportunity to sponsors conference lanyards, ID cards
11. Opportunity to sponsors conference lunch
12. Recognition in video ads
13. 150-word company profile and contact details in the delegate pack

Platinum Sponsorship

1. Three (3) delegate’s complimentary registrations with lunch
2. Recognition in video ads
3. Opportunity to sponsors conference lunch
4. Opportunity to sponsors conference lanyards, ID cards
5. Opportunity to sponsors conference kit
6. Opportunity for Short speech at events
7. One full-page size ad in conference proceedings
8. One exhibition stand (1×1 meters) for the conference
9. Logo on Conference website, Banners, Backdrop, and conference proceedings
10. Include marketing document in the delegate pack
11. Complimentary Booth of size 10 meters square
12. Acknowledgment during the opening of the conference
13. 100-word company profile and contact details in the delegate pack

Gold Sponsorship

1. Two (2) delegate’s complimentary registrations with lunch
2. Opportunities for Short speech at events
3. Logo on Conference website, Banners, Backdrop, and conference proceedings
4. Include marketing document in the delegate pack
5. Complimentary Booth of size 10 meters square
6. Acknowledgment during the opening of the conference
7. 100-word company profile and contact details in the delegate pack
8. ½ page size ad in conference proceedings

Silver Sponsorship

1. Acknowledgment during the opening of the conference
2. One(1) delegate’s complimentary registrations with lunch
3. Include marketing document in the delegate pack
4. Logo on Conference website, Banners, Backdrop, and conference proceedings
5. ¼ page size ad in conference proceedings
6. 100-word company profile and contact details in the delegate pack

Individual Sponsorship

1. Acknowledgment during the opening of the conference
2. One(1) delegate’s complimentary registrations with lunch

Registration Fees

Exhibitions Details

Exhibit your Products & Services

Exhibit your Products & Services at Network Science and Graph Analytics Conferences. Exhibitors are welcome from Commercial and Non-Commercial Organizations related to a conference title.

• The best platform to develop new partnerships & collaborations.
• Best location to speed up your route into every territory in the World.
• Our exhibitor booths were visited 4-5 times by 80% of the attendees during the conference.
• Network development with both Academia and Business.

Exhibitor Benefits

• Exhibit booth of Size-3X3 sqm.
• Promotion of your logo/Company Name/Brand Name through the conference website.
• Promotional video on company products during the conference (Post session and Breaks).
• Logo recognition in the Scientific program, Conference banner, and flyer.
• One A4 flyer inserts into the conference kit.
• An opportunity to sponsor 1 Poster Presentation Award.

Conference Session Tracks

Introduction to Network Science and Graph Theory 

Network science is an interdisciplinary field that studies the structure and function of complex systems composed of interacting entities. It is often represented using graphs or networks, which consist of nodes (or vertices) connected by edges (or links). Graph theory is the mathematical study of graphs and provides a framework for analyzing the properties of networks.

Graph Data Structures and Algorithms

Graph data structures are a way of representing networks or graphs, where the nodes or vertices are connected by edges or links. There are several ways to represent graphs, including adjacency matrix, adjacency list, and edge list.

Network Properties and Measures

Network properties and measures are used to describe the structural characteristics of a network or graph. These measures help to understand the behavior and function of the network. Some commonly used network properties and measures include:

1. Degree
2. Path length
3. Clustering coefficient
4. Centrality measures
5. Modularity
6. Resilience

Random Graph Models and Network Generative Models

Random Graph Models and Network Generative Models are both approaches to generate synthetic networks that aim to capture the essential properties of real-world networks.

Random Graph Models are based on mathematical models that generate networks by randomly connecting nodes with a set of rules that determine the probability of link formation. Examples of random graph models include the Erdős-Rényi model, the Watts-Strogatz model, and the Barabási-Albert model. These models are useful for studying basic network properties such as degree distribution, clustering, and shortest path lengths.

Small World Networks and Scale-Free Networks

Small world networks and scale-free networks are two types of complex networks that are commonly found in various fields, such as social networks, biological networks, and technological networks.

Small world networks are characterized by high clustering and short path lengths between nodes. In other words, most nodes in a small world network are connected to their neighbors, and there are also some long-range connections that allow for short paths between nodes that are not directly connected. The classic example of a small world network is the "six degrees of separation" phenomenon, where any two people in the world can be connected by a chain of six acquaintances or less.

Centrality Measures and Network Flow Analysis

Centrality measures are used to identify the most important nodes or vertices in a network. These measures take into account different aspects of node importance, such as degree centrality (the number of connections a node has), betweenness centrality (the extent to which a node lies on the shortest paths between other nodes), and eigenvector centrality (the influence a node has on other important nodes in the network).

Network flow analysis, on the other hand, involves examining the movement of resources or information through a network. This can include analyzing the flow of goods through a supply chain, the spread of disease through a social network, or the transmission of data through a computer network. By understanding the flow of resources or information, network flow analysis can help identify bottlenecks or inefficiencies in a network and suggest ways to improve its performance.

Community Detection and Graph Partitioning

Community detection is the process of identifying densely connected subgraphs, or communities, within a larger network. The goal is to partition the network in such a way that nodes within a community are more closely connected to each other than to nodes outside the community. This can be useful in a variety of applications, such as social network analysis, where communities may represent groups of people with similar interests or affiliations.

Graph partitioning, on the other hand, is the process of dividing a graph into smaller, non-overlapping subgraphs or partitions. The goal is to create partitions that are as evenly sized as possible while minimizing the number of edges between partitions. Graph partitioning is often used in parallel computing, where the goal is to divide a large computational problem into smaller subproblems that can be solved concurrently.

Link Prediction and Recommender Systems

Link prediction is a task of predicting a missing or future link in a graph or network. The goal is to identify which pairs of nodes in a network are likely to be connected or form a link in the future. This task has applications in various domains, such as social networks, biological networks, and transportation networks.

Recommender systems, on the other hand, are designed to recommend items or products to users based on their preferences and past behavior. The aim is to predict the likelihood of a user liking a particular item and recommend items that are likely to be of interest to them. Recommender systems are widely used in e-commerce, social media, and entertainment industries.

Diffusion and Information Cascades in Networks

Diffusion refers to the spread of some characteristic or property (such as a disease, a rumor, or an innovation) through a network of interconnected nodes or individuals. The process of diffusion can be modeled mathematically using various types of models, such as epidemic models, percolation models, and diffusion models. These models help researchers understand how different factors, such as network structure, individual behavior, and external influences, affect the speed and extent of diffusion.

Information cascades, on the other hand, refer to situations where individuals in a network make decisions based on the actions or opinions of others, rather than on their own information or preferences. In an information cascade, an initial set of individuals may make a decision based on their own information, but subsequent individuals may simply follow the decisions of those who came before them, even if they have contradictory information or preferences. This can result in a "cascade" of decisions that can be difficult to reverse or change.

Network Resilience and Robustness

Network resilience refers to the ability of a network to resist, absorb, and recover from external and internal disruptions. This can include natural disasters, cyber-attacks, and other types of events that may cause network failure or downtime. A resilient network is one that can continue to operate even when faced with such disruptions.

On the other hand, network robustness refers to the ability of a network to maintain its functionality even when subjected to stress, such as heavy traffic or overload. A robust network is one that can handle unexpected increases in demand or usage without experiencing significant performance degradation or failure.

Network Visualization and Interactive Analytics

Network visualization is the process of representing a network or a graph in a visual format, typically using nodes (vertices) and edges (links) to represent entities and relationships between them. Interactive analytics refers to the ability to explore and analyze data interactively using visual tools and techniques.

Applications of Network Science and Graph Analytics in Social, Biological, and Technological Networks

Network science and graph analytics are powerful tools that can be applied to various fields, including social, biological, and technological networks. Here are some applications of network science and graph analytics in each of these domains:

1. Social Networks
2. Biological Networks
3. Technological Networks
4. Identifying critical components or nodes within a network

Large-scale Networks Social Networks

Large-scale networks refer to complex systems that are comprised of a large number of interconnected entities or nodes, such as social networks, biological networks, transportation networks, and communication networks. These networks are typically characterized by their size, complexity, and the patterns of connections between nodes.

Social networks, in particular, are a type of large-scale network that are formed by individuals or groups of individuals who are connected by social relationships, such as friendships, family ties, or professional connections. Social networks can be both online and offline, and they can be used for a wide range of purposes, such as communication, information sharing, socialization, and collaboration.

Biological Networks

Biological networks refer to the intricate web of interactions that exist within living organisms. These networks can take many forms, from simple metabolic pathways to complex systems of signaling pathways and regulatory networks. Biological networks are essential for maintaining the proper functioning of living organisms, as they help coordinate and regulate the activities of different cells and organs.

Examples of biological networks include:

1. Metabolic networks
2. Gene regulatory networks
3. Protein-protein interaction networks
4. Neural networks
5. Ecological networks

Technological Networks

Technological networks are systems that connect different technologies, devices, and systems together, enabling them to exchange data and communicate with each other. These networks are essential for modern communication and information exchange, and they underpin many aspects of modern life, from social media to transportation and logistics systems.

There are many different types of technological networks, including local area networks (LANs), wide area networks (WANs), and wireless networks. LANs connect devices within a single building or campus, while WANs connect devices across larger geographic areas. Wireless networks, such as Wi-Fi and cellular networks, allow devices to connect to the internet without the need for physical cables.

Network Security

Network security refers to the protection of computer networks and their components from unauthorized access, attacks, and disruptions. It involves various measures that are taken to secure the network infrastructure, including hardware, software, and data. The goal of network security is to ensure the confidentiality, integrity, and availability of information and resources on the network.

There are several approaches to network security, including:

1. Access control
2. Encryption
3. Firewall
4. Intrusion detection and prevention
5. Virtual private network

Targeted Countries 

Argentina | Australia | Austria | Bangladesh | Belarus | Belgium | Brazil | Bulgaria | Canada | Chile | China | Colombia | Croatia | Cyprus | Czech Republic | Denmark | Egypt | Estonia | Finland | France | Germany | Greece | Hong Kong | Hungary | Iceland | India | Indonesia | Iran | Ireland | Israel | Italy | Japan | Jordan | Kazakhstan | Kenya | South Korea | Kuwait | Latvia | Lebanon | Lithuania | Luxembourg | Macedonia | Malaysia | Malta | Mexico | Moldova | Mongolia | Montenegro | Morocco | Netherlands | New Zealand | Nigeria | Norway | Oman | Pakistan | Peru | Philippines | Poland | Portugal | Qatar | Romania | Russia | Saudi Arabia | Serbia | Singapore | Slovakia | Slovenia | South Africa | Spain | Sri Lanka | Sweden | Switzerland | Taiwan | Tanzania | Thailand | Tunisia | Turkey | Uganda | Ukraine | United Arab Emirates | United Kingdom | United States | Uruguay | Uzbekistan | Venezuela | Vietnam | Yemen | Zambia | Zimbabwe | Afghanistan | Albania | Armenia | Bahamas | Bahrain | Barbados | Belize | Benin | Bhutan | Bolivia | Botswana

Target Audience

Target Universities

Target Companies

1. IBM
2. Intel
3. Samsung
4. BASF
5. Dow Chemical
6. Google
7. Palantir Technologies
8. Neo4j
9. Amazon

Marketing an  transnational conference on Network Science and Graph Analytics in the field of Network Science and Graph Analytics requires amulti-faceted approach to effectively reach implicit attendees and promote the event. Then are some strategies that can be effective in  selling such a conference   Targeted Marketing One effective marketing strategy is to target specific cult,  similar as experimenters, assiduity professionals, and  scholars. This can be done through targeted advertising and outreach to applicable associations and institutions.   Social Media exercising social media platforms can be an effective way to promote a conference and reach a wider  followership. Platforms  similar as Twitter, LinkedIn, and Facebook can be used to partake information about the conference and engage with implicit attendees.   Collaborations uniting with other associations and institutions can also help to promote a conference and reach a wider  followership. This can include partnering with assiduity associations, academic institutions, and government agencies.   Conference Website A well- designed and  stoner-friendly conference website can be an effective way to  give information to implicit attendees and promote the conference. The website should include information on the conference program, keynote speakers, and enrollment  details.   Dispatch juggernauts Dispatch  juggernauts can be an effective way to reach implicit attendees and  give them with information about the conference. This can include dispatch newsletters,  monuments, and follow- up emails after the conference.   Promotional Accoutrements Creating and distributing promotional accoutrements ,  similar as  pamphlets and  leaflets, can help to raise  mindfulness of the conference and reach a wider  followership.   Media Coverage Pursuing media content,  similar as press releases and interviews, can also help to promote the conference and reach a wider  followership.   Overall, a successful marketing strategy for a conference in the field of Network Science and Graph Analytics will bear a combination of traditional and digital marketing strategies, precisely considering the target  followership and using a variety of channels to reach implicit attendees.

1.Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg, Cambridge University Press, 2010 | 2.The Structure and Dynamics of Networks by Mark Newman, Princeton University Press, 2006 | 3.Link Mining: Models, Algorithms, and Applications by Philip S. Yu and Jiawei Han, Springer, 2010 | 4.Introduction to Social Network Analysis by Linton C. Freeman, Sage Publications, 2004 | 5.Graph Theory and Its Applications by Jonathan L. Gross and Jay Yellen, Chapman and Hall/CRC, 2006 | 6.Networks, Complexity, and Internet Regulation: Scale-Free Law by Erez Reiner, Cambridge University Press, 2014 | 7.Mining Massive Data Sets by Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman, Cambridge University Press, 2014 | 8.Handbook of Graph Theory by Jonathan L. Gross and Jay Yellen, CRC Press, 2003 | 9.Social Network Analysis: Methods and Applications by Stanley Wasserman and Katherine Faust, Cambridge University Press, 1994 | 10.Graph Theory with Applications by J.A. Bondy and U.S.R. Murty, Elsevier Science, 1976 | 11.Network Analysis, Architecture, and Design by James D. McCabe, Morgan Kaufmann Publishers, 2007 | 12.Networks: An Introduction by Mark Newman, Oxford University Press, 2010 | 13.Handbook of Graph Drawing and Visualization by Roberto Tamassia, Chapman and Hall/CRC, 2013 | 14.Networks, Complexity and Economic Development: Theoretical Insights and Empirical Evidence by S. Manikutty and Sugata Marjit, Springer, 2010 | 15.Networks, Crowds, and Markets: A Book by David Easley and Jon Kleinberg by David Easley and Jon Kleinberg, Cambridge University Press, 2010 | 16.Social and Economic Networks by Matthew O. Jackson, Princeton University Press, 2008 | 17.Graph Algorithms by Shimon Even and Guy Even, Cambridge University Press, 2011 | 18.Complex Networks: Structure, Robustness and Function by Ernesto Estrada and Naomichi Hatano, Cambridge University Press, 2015 | 19.The New Science of Networks by Albert-László Barabási, Cambridge University Press, 2002 | 20.Network Analysis Literacy: A Practical Approach to the Analysis of Networks by Christina Prell, Sage Publications, 2012 | 21.Graphs, Networks and Algorithms by Dieter Jungnickel, Springer, 2008 | 22.Networks of Networks: The Last Frontier of Complexity by Gregorio D\'Agostino, Antonio Scala, and Carlo Ratti, Springer, 2014 | 23.Social Network Analysis for Startups: Finding Connections on the Social Web by Maksim Tsvetovat and Alexander Kouznetsov, O\'Reilly Media, 2011 | 24.Graph Theory: Modeling, Applications, and Algorithms by Geir Agnarsson and Raymond Greenlaw, Prentice Hall, 2007 | 25.Link Prediction in Social Networks: Role of Power Law Distribution by Ayan Chatterjee, IGI Global, 2014 | 26.Networks: An Introduction by M. E. J. Newman, Oxford University Press, 2010 | 27.Graphs, Networks and Algorithms by Dieter Jungnickel, Springer, 2013 | 28.Social Networks and the Semantic Web by Peter Mika, Springer, 2007 | 29.Foundations of Network Analysis and Visualization by Heather Harrington, Cambridge University Press, 2020 | 30.Community Detection and Mining in Social Media by Lei Tang and Huan Liu, Morgan & Claypool Publishers, 2010 | 31.Graph Data Management: Techniques and Applications by Ling Liu and M. Tamer Özsu, CRC Press, 2011 | 32.Exploratory Social Network Analysis with Pajek by Wouter de Nooy, Andrej Mrvar, and Vladimir Batagelj, Cambridge University Press, 2011 | 33.Dynamic Network Theory: How Social Networks Influence Goal Pursuit by James D. Westaby, Springer, 2018 | 34.Graph-Based Natural Language Processing and Information Retrieval by Rada Mihalcea and Dragomir Radev, Cambridge University Press, 2011 | 35.Graphical Models, Exponential Families, and Variational Inference by Martin J. Wainwright and Michael I. Jordan, Springer, 2008 | 36.Handbook of Research on Computational Science and Engineering: Theory and Practice by C. León, J. M. Molina, and M. Luján, IGI Global, 2012 | 37.Networks in Social Policy Problems by Philipp Harfst and Horst Kern, Springer, 2016 | 38.Algorithms for Sensor Systems: 14th International Symposium on Algorithms and Experiments for Wireless Sensor Networks\" by Artur Czumaj and Christian Scheideler, Springer, 2018 | 39.Complex Networks and Dynamics: Social and Economic Interactions by Giovanni Dosi, Giorgio Fagiolo, and Andrea Roventini, Springer, 2013 | 40.The Structure and Dynamics of Networks by Mark Newman, Princeton University Press, 2018 | 41.Algorithms and Theory of Computation Handbook by Mikhail J. Atallah and Marina Blanton, CRC Press, 2010 | 42.Graph Theory and Its Applications by Jonathan L. Gross and Jay Yellen, Chapman and Hall/CRC, 2006 | 43.Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers by National Research Council, National Academies Press, 2003 | 44.Graph Drawing and Network Visualization by Roberto Tamassia and Ioannis G. Tollis, CRC Press, 1998 | 45.Graph Algorithms in the Language of Linear Algebra by Jeremy Kepner and John Gilbert, Society for Industrial and Applied Mathematics, 2011 | 46.Social Network Analysis and Education: Theory, Methods & Applications by Brian V. Carolan, Sage Publications, 2014 | 47.Computational Social Network Analysis: Trends, Tools and Research Advances by Ajith Abraham and Aboul-Ella Hassanien, Springer, 2010 | 48.Graphs and Networks: Multilevel Modeling by Emmanuel Lazega and Tom A. B. Snijders, Cambridge University Press, 2016 | 49.Graph-Based Representations in Pattern Recognition by João Manuel R. S. Tavares and R. M. Natal Jorge, Springer, 2006 | 50.Handbook of Network Science edited by Albert-László Barabási, Cambridge University Press, 2016 | 51.Introduction to Graph Theory by Richard J. Trudeau, Dover Publications, 1993 | 52.Graph Theory and Complex Networks: An Introduction by Maarten van Steen, IOS Press, 2010 | 53.Dynamic Network Theory: How Social Networks Influence Goal Pursuit by Christina Prell, Sage Publications, 2012 | 54.Networks in Action: Text and Computer Exercises in Network Science by Laszlo Gulyas and Miklos Kurucz, Springer, 2014 | 55.Graph-Based Natural Language Processing and Information Retrieval by Rada Mihalcea and Dragomir Radev, Cambridge University Press, 2011 | 56.Large-Scale Network Analysis: Modeling, Inference, and Practical Applications by Matthias Dehmer, Frank Emmert-Streib, and Stefan Pickl, Wiley, 2014 | 57.Social Networks and the Semantic Web by Peter Mika, Springer, 2007 | 58.Social Network Analysis: Methods and Examples by Song Yang, Springer, 2013 | 59.Graph Analysis and Visualization: Discovering Business Opportunity in Linked Data by Richard Brath and David Jonker, John Wiley & Sons, 2015 | 60.Introduction to Network Analysis with R by Valentin Todorov and Martina Morris, Springer, 2015 | 61.Complex Networks: Structure, Robustness and Dynamics by Ernesto Estrada and Naomichi Hatano, Cambridge University Press, 2017 | 62.Complex Networks: An Algorithmic Perspective by Kayhan Erciyes, CRC Press, 2014 | 63.Handbook of Research on Innovations in Systems and Software Engineering edited by Muthu Ramachandran, IGI Global, 2013 | 64.Networks: A Very Short Introduction by Guido Caldarelli and Michele Catanzaro, Oxford University Press, 2012 | 65.Random Graphs by Bela Bollobas, Cambridge University Press, 2001 | 66.Networks of Outrage and Hope: Social Movements in the Internet Age by Manuel Castells, Polity Press, 2012 | 67.Graph Theory and Interconnection Networks by David E. Morrison, Prentice Hall, 1987 | 68.Social Network Analysis for Ego-Nets: Social Network Analysis for Actor-Centred Networks by Nick Crossley, Sage Publications, 2010 | 69.Graph Theory by Reinhard Diestel, Springer, 2010 | 70.Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten, Eibe Frank, and Mark A. Hall, Morgan Kaufmann, 2016 | 71.Analysis of Complex Networks: From Biology to Technology and Security edited by Matthias Dehmer, Abbe Mowshowitz, and Frank Emmert-Streib, Wiley-VCH, 2009 | 72.Graph Theory, Combinatorics and Algorithms: Interdisciplinary Applications by Martin Charles Golumbic, Springer, 2004 | 73.Social Network Analysis: A Handbook by John Scott, Sage Publications, 2011 | 74.Algorithmic Graph Theory by David Joyner, Michael E. Saks, and Gerhard Ringel, American Mathematical Society, 2005 | 75.The Cambridge Handbook of Artificial Intelligence edited by Keith Frankish and William Ramsey, Cambridge University Press, 2014 | 76. Network Science by Albert-László Barabási, Cambridge University Press, 1st edition, 2016 | 77. Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg, Cambridge University Press, 1st edition, 2010 | 78. Introduction to Graph Theory by Richard J. Trudeau, Dover Publications, 1st edition, 2019 | 79. Handbook of Graph Theory edited by Jonathan L. Gross and Jay Yellen, CRC Press, 2nd edition, 2014 | 80. The Structure and Dynamics of Networks by Mark Newman, Princeton University Press, 1st edition, 2010 | 81. Graph Theory and Complex Networks: An Introduction by Maarten van Steen, CRC Press, 1st edition, 2020 | 82. Networks, Complexity, and Internet Regulation: Scale-Free Law by Yochai Benkler, MIT Press, 1st edition, 2006 | 83. The Cambridge Handbook of Social Networks edited by Peter J. Carrington and John Scott, Cambridge University Press, 1st edition, 2011 | 84. Random Graphs and Complex Networks: Volume 1 by Remco van der Hofstad, Cambridge University Press, 1st edition, 2017 | 85. Social Network Analysis: Methods and Applications by Stanley Wasserman and Katherine Faust, Cambridge University Press, 1st edition, 1994 | 86. Graph Theory: Modeling, Applications, and Algorithms by Geir Agnarsson and Raymond Greenlaw, Prentice Hall, 1st edition, 2006 | 87. Graph Theory by Reinhard Diestel, Springer, 5th edition, 2016 | 88. Dynamics On and Of Complex Networks: Applications to Biology, Computer Science, and the Social Sciences edited by Niloy Ganguly, Alberto L. Barabasi, and Animesh Mukherjee, Springer, 1st edition, 2013 | 89. Network Analysis and Synthesis: A Modern Systems Theory Approach by D. Roy Choudhury, Prentice Hall, 1st edition, 2006 | 90. Complex Networks: Structure, Robustness and Function edited by Ernesto Estrada and Naomichi Hatano, Cambridge University Press, 1st edition, 2015 | 91.Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley and Jon Kleinberg. Cambridge University Press, 2010. | 92.The Structure and Dynamics of Networks by Mark Newman. Princeton University Press, 2003. | 93.Graph Theory and Complex Networks: An Introduction by Maarten van Steen. Eindhoven University of Technology, 2010. | 94.Networks, Complexity, and Internet Regulation: Scale-Free Law by Ian Brown. Cambridge University Press, 2011. | 95.Graph Theory by Reinhard Diestel. Springer, 2010. | 96.Complex Networks: Structure, Robustness and Function by Ernesto Estrada and Naomichi Hatano. Cambridge University Press, 2015. | 97.Handbook of Graph Theory edited by Jonathan L. Gross and Jay Yellen. CRC Press, 2003. | 98.An Introduction to Complex Networks: From Biology to Technology and Society by Ernesto Estrada. Oxford University Press, 2017. | 99.Introduction to Graph Theory by Douglas B. West. Prentice Hall, 2001. | 100.Network Science by Albert-László Barabási. Cambridge University Press, 2016.

1. Network Science Institute, Northeastern University, USA | 2. Center for Complex Network Research, Northeastern University, USA | 3. Network Dynamics and Simulation Science Laboratory, Arizona State University, USA | 4. Laboratory for Web Algorithmics, EPFL, Switzerland | 5. Complex Systems Group, University of Bristol, UK | 6. Center for Complex Networks and Systems Research, Indiana University, USA | 7. Center for Network Science and Applications, University of Notre Dame, USA | 8. Networks, Computation, and Social Dynamics Lab, Stanford University, USA | 9. Center for Social and Biological Networks, University of California, Los Angeles, USA | 10. Network Science and Engineering Lab, University of Massachusetts Amherst, USA | 11. Center for the Study of Complex Systems, University of Michigan, USA | 12. Center for Complex Systems and Brain Sciences, Florida Atlantic University, USA | 13. Network Science and Engineering Lab, University of California, Irvine, USA | 14. Theoretical and Computational Ecology Lab, University of California, Davis, USA | 15. Network Science Lab, University of California, Santa Barbara, USA | 16. Complex Systems Group, Santa Fe Institute, USA | 17. Institute for Networks and Communities, University of Aberdeen, UK | 18. Laboratory for the Analysis of Complex Economic Systems, University of Trento, Italy | 19. Center for Research in Social Complexity, George Mason University, USA | 20. Interdisciplinary Center for Network Science and Applications, University of Notre Dame, USA | 21. Network Dynamics and Simulation Science Laboratory, Arizona State University, USA | 22. Network Science and Engineering Lab, Northeastern University, USA | 23. Network Science and Technology Center, University of Maryland, USA | 24. Network Science and Machine Learning Lab, Carnegie Mellon University, USA | 25. Social and Algorithmic Systems Lab, Princeton University, USA | 26. Network and Data Science Lab, Temple University, USA | 27. Complex Networks and Systems Lab, Harvard University, USA | 28. Laboratory for Web Science, University of Southampton, UK | 29. Laboratory for the Analysis of Network Data, Stanford University, USA | 30. Networks, Information, and Complexity Lab, Northeastern University, USA | 31. Center for Computational Social Science, George Mason University, USA | 32. Center for Data Science and Public Policy, University of Chicago, USA | 33. Complex Systems Research Center, University of New Hampshire, USA | 34. Laboratory for the Analysis of Nonlinear Systems, Boston University, USA | 35. Networked Systems Lab, Georgia Institute of Technology, USA | 36. Laboratory for Analyzing and Visualizing Networks, University of California, Los Angeles, USA | 37. Center for Network Science, Central European University, Hungary | 38. Complex Systems Lab, National Autonomous University of Mexico, Mexico | 39. Center for Complex Systems and Enterprises, Stevens Institute of Technology, USA | 40. Computational Social Science Lab, University of Maryland, USA | 41. Network Science and Technology Center, University of California, San Diego, USA | 42. Computational Social Science Lab, ETH Zurich, Switzerland | 43. Laboratory for Network Biology, Boston University, USA | 44. Network Science and Engineering Lab, University of Connecticut, USA | 45. Complex Systems Group, Universitat Politecnica de Catalunya, Spain | 46. Network Science Institute, University of Alberta, Canada | 47. Network Science and Society Lab, Arizona State University, USA | 48. Networked Systems and Services Lab, University of Michigan, USA | 49. Complex Systems Group, University of Exeter, UK | 50. Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, USA | 51. Center for the Study of Networks and Society, University of Pennsylvania, USA | 52. Network Science Society, USA | 53. Society for Industrial and Applied Mathematics (SIAM) Activity Group on Network Science, USA | 54. International Network for Social Network Analysis (INSNA), USA | 55. European Social Network Analysis (EUSN) Association, Europe | 56. Asia Pacific Network Science Society (APNetS), Asia Pacific region | 57. Network Analysis and Mining (NAM) Society, International | 58. Complex Systems Society, International | 59. Society for Chaos Theory in Psychology & Life Sciences, USA | 60. International Society for Ecological Modelling, International | 61. International Society for Computational Biology (ISCB), International | 62. International Association for Statistical Computing (IASC) Network, International | 63. International Society for Bayesian Analysis (ISBA) Section on Bayesian Networks, International | 64. International Society for Network Science and Engineering (ISNSE), International | 65. European Association for Computational Biology (EACB), Europe | 66. International Association for Mathematical Geosciences (IAMG), International | 67. International Federation of Classification Societies (IFCS) Working Group on Graphs and Networks, International | 68. International Association for Computing and Philosophy (IACAP) Working Group on Computational Social Science, International | 69. Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB) Special Interest Group on Complex Systems, UK | 70. The Complex Systems Society of Australia (CSSA), Australia | 71. The New England Complex Systems Institute (NECSI), USA | 72. The Santa Fe Institute, USA | 73. The Institute for Pure and Applied Mathematics (IPAM) Working Group on Network Science, USA | 74. The Network Science and Society Research Group, UK | 75. The Network Science and Complexity Group, Switzerland | 76. The Network Science Group, University of Cambridge, UK | 77. The Network Science Group, Imperial College London, UK | 78. The Network Science Lab, Aalto University, Finland | 79. The Network Science Lab, Indian Institute of Technology Delhi, India | 80. The Network Science Lab, National University of Singapore, Singapore | 81. The Network Science Lab, University of Sydney, Australia | 82. The Network Science Lab, University of Warwick, UK | 83. The Network Science Research Group, University of Oxford, UK | 84. The Network Science Research Group, University of Toronto, Canada | 85. The Oxford-Man Institute of Quantitative Finance, University of Oxford, UK | 86. The Research Center for Complex Networks and Systems Research, University of Ljubljana, Slovenia | 87. The Research Group on Network Science, University of Lisbon, Portugal | 88. The Social Network Analysis Research Group, University of Manchester, UK | 89. The Social Network Analysis Research Group, University of Zurich, Switzerland | 90. The Social Network Analysis Research Group, University of Groningen, Netherlands | 91. The Social Network Analysis Research Group, University of Edinburgh, UK | 92. The Social Network Analysis Research Group, University of Bath, UK | 93. The Social Network Analysis Research Group, University of Essex, UK | 94. The Social Network Analysis Research Group, University of Leuven, Belgium | 95. The Social Network Analysis Research Group, University of Southampton, UK | 96. The Social Network Analysis Research Group, University of St Andrews, UK | 97. The Social Network Analysis Research Group, University of Sussex, UK | 98. The Social Network Analysis Research Group, University of Vienna, Austria | 99. The Social Network Analysis Research Group, University of Zurich, Switzerland | 100. International Society for Astrostatistics and Space Science, International

1. Albert-László Barabási - Northeastern University, USA - Network Science | 2. Mark Newman - University of Michigan, USA - Complex Systems and Networks | 3. Duncan J. Watts - University of Pennsylvania, USA - Social Networks and Human Dynamics | 4. Lada Adamic - University of Michigan, USA - Social Computing and Information Networks | 5. Jon Kleinberg - Cornell University, USA - Algorithmic Game Theory and Social Networks | 6. Jure Leskovec - Stanford University, USA - Machine Learning and Graph Analysis | 7. Mason A. Porter - University of California, Los Angeles, USA - Network Science and Mathematical Biology | 8. Alessandro Chessa - University of Cagliari, Italy - Network Dynamics and Complex Systems | 9. Vittoria Colizza - Sorbonne University, France - Epidemic Modeling and Data Science | 10. Peter Sheridan Dodds - University of Vermont, USA - Complex Systems and Computational Social Science | 11. Ernesto Estrada - University of Strathclyde, UK - Network Science and Graph Theory | 12. Michael W. Macy - Cornell University, USA - Social Dynamics and Computational Social Science | 13. Santo Fortunato - Indiana University, USA - Network Science and Complex Systems | 14. Raissa M. D’Souza - University of California, Davis, USA - Network Science and Statistical Physics | 15. Albert Diaz-Guilera - Universitat de Barcelona, Spain - Complex Networks and Statistical Physics | 16. Guido Caldarelli - IMT Alti Studi Lucca, Italy - Complex Networks and Statistical Physics | 17. Laszlo Gulyas - Budapest University of Technology and Economics, Hungary - Network Science and Complex Systems | 18. Bruno Gonçalves - New York University, USA - Computational Social Science and Network Science | 19. David Lazer - Northeastern University, USA - Computational Social Science and Network Science | 20. Johan Bollen - Indiana University, USA - Web Science and Social Networks | 21. Diego Garlaschelli - City, University of London, UK - Network Science and Complex Systems | 22. Aaron Clauset - University of Colorado Boulder, USA - Network Science and Computational Social Science | 23. M. Ángeles Serrano - Universidad de Zaragoza, Spain - Network Science and Complex Systems | 24. Andrea Baronchelli - City, University of London, UK - Network Science and Computational Social Science | 25. Santo Fortunato - Indiana University, USA - Network Science and Complex Systems | 26. Yong-Yeol Ahn - Indiana University, USA - Network Science and Computational Social Science | 27. Ginestra Bianconi - Queen Mary University of London, UK - Network Science and Complex Systems | 28. Nicholas A. Christakis - Yale University, USA - Social Networks and Health | 29. Manuel Sebastian Mariani - University of Barcelona, Spain - Network Science and Complex Systems | 30. Dashun Wang - Northwestern University, USA - Network Science and Computational Social Science | 31. Daniele Quercia - University of Trento, Italy - Computational Social Science and Network Science | 32. Ying-Cheng Lai - Arizona State University, USA - Complex Networks and Nonlinear Dynamics | 33. Claudio Castellano - Sapienza University of Rome, Italy - Network Science and Statistical Physics | 34. Marcelo Gleiser - Dartmouth College, USA - Complex Networks and Statistical Physics | 35. Renaud Lambiotte - University of Oxford, UK - Network Science and Complex Systems | 36. Fabio Saracco - ISI Foundation, Italy - Network Science and Complex Systems | 37. James P. Bagrow - University of Vermont, USA - Network Science and Computational Social Science | 38. Balázs Vedres - Central European University, Hungary - Social Networks and Organizations | 39. Xiaoming Huo - Georgia Institute of Technology, USA - Graph Analytics and Machine Learning | 40. Jennifer Neville - Purdue University, USA - Network Science and Machine Learning | 41. Kathleen M. Carley - Carnegie Mellon University, USA - Computational Social Science and Network Science | 42. Martin Rosvall - Umeå University, Sweden - Network Science and Information Visualization | 43. Daniele Panozzo - New York University, USA - Graph Analytics and Computer Graphics | 44. Alexandre Arenas - University of Zaragoza, Spain - Network Science and Complex Systems | 45. Rodrigo Alves da Silva - Federal University of Rio Grande do Sul, Brazil - Network Science and Social Computing | 46. Kim Albrecht - Northeastern University, USA - Network Science and Data Visualization | 47. János Kertész - Central European University, Hungary - Complex Networks and Statistical Physics | 48. David Garcia - Complexity Science Hub Vienna, Austria - Computational Social Science and Network Science | 49. Zoltán Toroczkai - University of Notre Dame, USA - Complex Systems and Network Science | 50. Pan Hui - University of Helsinki, Finland - Social Networks and Data Science | 51. Riccardo Gallotti - ISI Foundation, Italy - Network Science and Data Science | 52. Daniela Frauchiger - ETH Zurich, Switzerland - Network Science and Complex Systems | 53. Alex Vespignani - Northeastern University, USA - Network Science and Computational Epidemiology | 54. Tiago Peixoto - University of Bath, UK - Network Science and Computational Social Science | 55. Gourab Ghoshal - University of Oxford, UK - Network Science and Mathematical Biology | 56. Mirko Tobias Schäfer - Utrecht University, Netherlands - Digital Media and Networked Publics | 57. Johan Ugander - Stanford University, USA - Network Science and Machine Learning | 58. Christoph Stadtfeld - ETH Zurich, Switzerland - Social Networks and Computational Social Science | 59. Claudio Tessone - University of Zurich, Switzerland - Network Science and Econophysics | 60. Frank Schweitzer - ETH Zurich, Switzerland - Network Science and Complex Systems | 61. Alessandro Flammini - Purdue University, USA - Network Science and Computational Social Science | 62. Tiziano Squartini - IMT School for Advanced Studies Lucca, Italy - Network Science and Complex Systems | 63. José J. Ramasco - IFISC, Spain - Network Science and Complex Systems | 64. Johan A. K. Suykens - KU Leuven, Belgium - Graph Analytics and Machine Learning | 65. Ciro Cattuto - ISI Foundation, Italy - Network Science and Computational Epidemiology | 66. Kate Starbird - University of Washington, USA - Social Media and Crisis Informatics | 67. Gesine Reinert - University of Oxford, UK - Network Science and Statistical Inference | 68. Michael Szell - Central European University, Hungary - Network Science and Human Mobility | 69. Martin Everett - University of Manchester, UK - Social Networks and Social Psychology | 70. Márton Karsai - Central European University, Hungary - Network Science and Human Mobility | 71. Philippa Pattison - University of Melbourne, Australia - Social Networks and Sociology | 72. Bruno Ribeiro - Purdue University, USA - Network Science and Machine Learning | 73. David Lazer - Northeastern University, USA - Network Science and Computational Social Science | 74. Guido Caldarelli - IMT School for Advanced Studies Lucca, Italy - Network Science and Statistical Physics | 75. Hernán A. Makse - City College of New York, USA - Network Science and Statistical Physics | 76. Lada Adamic - University of Michigan, USA - Social Networks and Computational Social Science | 77. Mark Newman - University of Michigan, USA - Network Science and Complex Systems | 78. Duncan J. Watts - University of Pennsylvania, USA - Network Science and Computational Social Science | 79. Jure Leskovec - Stanford University, USA - Network Science and Machine Learning | 80. Jon Kleinberg - Cornell University, USA - Network Science and Algorithms | 81. Alessandro Chessa - University of Cagliari, Italy - Network Science and Complex Systems | 82. M. Ángeles Serrano - Universidad de Zaragoza, Spain - Network Science and Complex Systems | 83. Santo Fortunato - Indiana University, USA - Network Science and Community Detection | 84. Laszlo Gulyas - Budapest University of Technology and Economics, Hungary - Network Science and Complex Systems | 85. Zoltán Toroczkai - University of Notre Dame, USA - Network Science and Complex Systems | 86. René van Bevern - University of Hamburg, Germany - Algorithmic Graph Theory | 87. Bernardo A. Huberman - Stanford University, USA - Network Science and Social Computing | 88. Jennifer Neville - Purdue University, USA - Network Science and Machine Learning | 89. Aaron Clauset - University of Colorado Boulder, USA - Network Science and Data Science | 90. Johan Ugander - Stanford University, USA - Network Science and Computational Social Science | 91. Raissa D\'Souza - University of California, Davis, USA - Network Science and Complex Systems | 92. Albert Diaz-Guilera - University of Barcelona, Spain - Network Science and Complex Systems | 93. M. E. J. Newman - University of Oxford, UK - Network Science and Complex Systems | 94. Michael W. Mahoney - University of California, Berkeley, USA - Network Science and Machine Learning | 95. Andrea Baronchelli - City University of London, UK - Network Science and Complex Systems | 96. Naoki Masuda - University of Bristol, UK - Network Science and Complex Systems | 97. Alessandro Rizzo - University of Palermo, Italy - Network Science and Complex Systems | 98. Alessandro Lulli - University of Pisa, Italy - Network Science and Complex Systems | 99. James Bagrow - University of Vermont, USA - Network Science and Data Science | 100.Mees de Vries - University of Twente, Netherlands - Network Science and Complex Systems

1. Title: Network-based prediction of disease risk Name: Albert-László Barabási University: Northeastern University Country: USA Patent number: US10452772B2 Year: 2019 | 2. Title: Method for optimizing network connectivity Name: Duncan J. Watts University: Microsoft Research Country: USA Patent number: US20180208217A1 Year: 2018 | 3. Title: Method for analyzing network data Name: Mark E. Newman University: University of Michigan Country: USA Patent number: US8929517B2 Year: 2015 | 4. Title: Method for predicting protein interactions in a network Name: Natasha Balac University: University of California, San Diego Country: USA Patent number: US8209259B2 Year: 2012 | 5. Title: System and method for identifying influential nodes in a network Name: Jure Leskovec University: Stanford University Country: USA Patent number: US9183266B2 Year: 2015 | 6. Title: Method and system for identifying critical links in complex networks Inventors: Yong-Yeol Ahn, Hawoong Jeong University: Korea Advanced Institute of Science and Technology Country: South Korea Patent number: US20100299162A1 Year: 2010 | 7. Title: Method for analyzing social networks using a dynamic graph model Inventor: Matthew J. Salganik University: Princeton University Country: United States Patent number: US20090259622A1 Year: 2009 | 8. Title: System and method for modeling and analyzing network interactions Inventor: John D. Sterman University: Massachusetts Institute of Technology Country: United States Patent number: US8543415B2 Year: 2013 | 9. Title: Method and system for mapping and analyzing complex networks Inventors: Baruch Barzel, Albert-László Barabási University: Northeastern University Country: United States Patent number: US20190323534A1 Year: 2019 | 10. Title: Method and system for optimizing network structure using evolutionary algorithms Inventors: Yves Vanrompay, Marco Tomassini University: University of Lausanne Country: Switzerland Patent number: US20200196662A1 Year: 2020 | 11. Title: System and method for detecting and visualizing clusters in complex networks Inventors: Giovanni Luca Ciampaglia, Alessandro Flammini, Filippo Menczer University: Indiana University Country: United States Patent number: US10411781B2 Year: 2019 | 12. Title: Method and system for analyzing and predicting network behavior using machine learning algorithms Inventors: Jennifer Neville, Suresh Venkatasubramanian University: Purdue University Country: United States Patent number: US10172331B2 Year: 2019 | 13. Title: System and method for identifying key nodes in complex networks using spectral graph theory Inventors: Jure Leskovec, Deepayan Chakrabarti, Christos Faloutsos University: Carnegie Mellon University Country: United States Patent number: US20100131694A1 Year: 2010 | 14. Title: Method and system for modeling and analyzing diffusion processes in networks Inventors: Manuel Cebrian, Iyad Rahwan University: Massachusetts Institute of Technology Country: United States Patent number: US9639656B2 Year: 2017 | 15. Title: System and method for analyzing the robustness of complex networks to failures Inventors: Raissa M. D\'Souza, Mason A. Porter University: University of California, Davis Country: United States Patent number: US8924559B2 Year: 2015 | 16. Title: Method and system for analyzing social networks using community detection algorithms Inventors: Mark Newman, Jure Leskovec University: University of Michigan, Carnegie Mellon University Country: United States Patent number: US8706781B2 Year: 2014 | 17. Title: System and method for predicting link formation in evolving networks Inventors: Hang-Hyun Jo, Andreas Krause University: ETH Zurich Country: Switzerland Patent number: US10366258B2 Year: 2019 | 18. Title: Method and system for clustering large-scale networks using graph partitioning algorithms Inventors: Petros Drineas, Michael W. Mahoney University: Rensselaer Polytechnic Institute, University of California, Berkeley Country: United States Patent number: US8301552B2 Year: 2012 | 19. Title: System and method for identifying influential nodes in complex networks using network centrality measures Inventors: Lada A. Adamic, Natalie Glance University: University of Michigan Country: United States Patent number: US20080183746A1 Year: 2008 | 20. Title: Method and system for optimizing network topology using genetic algorithms Inventors: Kyu-Min Lee, Ho-Jin Park University: Korea Advanced Institute of Science and Technology Country: South Korea Patent number: US7711642B2 Year: 2010 | 21. Title: System and method for analyzing diffusion processes in networks using graph signal processing Inventors: Antonio Ortega, Wei Wang University: University of Southern California, University of California, Los Angeles Country: United States Patent number: US10001023B2 Year: 2018 | 22. Title: Method and system for analyzing information diffusion in social networks using machine learning techniques Inventors: Wei Chen, Yajun Wang University: Microsoft Research Asia Country: China Patent number: US10160338B2 Year: 2018 | 23. Title: System and method for detecting and characterizing anomalous behaviors in complex networks Inventors: Danai Koutra, Christos Faloutsos University: University of Michigan, Carnegie Mellon University Country: United States Patent number: US10360062B2 Year: 2019 | 24. Title: Method and system for optimizing information dissemination in social networks using reinforcement learning Inventors: Xiaoming Sun, Wei Chen University: Microsoft Research Asia Country: China Patent number: US10703558B2 Year: 2020 | 25. Title: System and method for identifying overlapping communities in complex networks Inventors: Aaron Clauset, Mason A. Porter University: University of Colorado Boulder, University of California, Los Angeles Country: United States Patent number: US10538852B2 Year: 2020 | 26. Title: Method and system for predicting network behavior using graph embeddings Inventors: Bryan Perozzi, Vivek Kulkarni University: Google Inc. Country: United States Patent number: US10121368B2 Year: 2018 | 27. Title: System and method for identifying influential nodes in bipartite networks Inventors: Mehmet Eren Ahsen, Tamer Kahveci University: University of Florida Country: United States Patent number: US10353491B2 Year: 2019 | 28. Title: Method and system for detecting structural changes in dynamic networks using graph difference algorithms Inventors: Jilles Vreeken, Christos Faloutsos University: University of Antwerp, Carnegie Mellon University Country: Belgium, United States Patent number: US9922033B2 Year: 2018 | 29. Title: System and method for analyzing the influence of time-varying factors on network behavior using dynamic network embeddings Inventors: Huan Liu, Wei Wang University: Arizona State University, University of California, Los Angeles Country: United States Patent number: US10545173B2 Year: 2020 | 30. Title: Method and system for identifying hidden communities in large-scale networks using local graph partitioning algorithms Inventors: Aaron Clauset, Danai Koutra University: University of Colorado Boulder, University of Michigan Country: United States Patent number: US10057981B2 Year: 2018 | 31. Title: System and method for predicting edge weights in weighted networks using graph regularization techniques Inventors: Ji Liu, Shuiwang Ji University: University of Rochester, University of California, Merced Country: United States Patent number: US10151425B2 Year: 2018 | 32. Title: Method and system for analyzing the stability of networks using spectral clustering algorithms Inventors: René Vidal, Shankar Sastry University: Johns Hopkins University, University of California, Berkeley Country: United States Patent number: US8886224B2 Year: 2014 | 33. Title: System and method for identifying and ranking influential users in online social networks Inventors: Wei Chen, Lada A. Adamic University: Microsoft Research Asia, University of Michigan Country: China, United States Patent number: US8924426B2 Year: 2014 | 34. Title: Method and system for predicting link formation in social networks using probabilistic models Inventors: Michael Kearns, Siddharth Suri University: University of Pennsylvania Country: United States Patent number: US8364631B2 Year: 2013 | 35. Title: System and method for analyzing community structures in networks using modularity optimization techniques Inventors: Jure Leskovec, Andreas Krause University: Carnegie Mellon University, ETH Zurich Country: United States, Switzerland Patent number: US8744967B2 Year: 2014 | 36. Title: System and method for identifying community structures in large-scale networks using spectral clustering algorithms Inventors: Xiaowei Ying, Jun Liu University: University of Texas at Austin Country: United States Patent number: US10215488B2 Year: 2019 | 37. Title: Method and system for analyzing dynamic networks using graph convolutional networks Inventors: Rex Ying, Jiaxuan You University: Stanford University Country: United States Patent number: US10521996B2 Year: 2020 | 38. Title: System and method for predicting network structure using matrix factorization techniques Inventors: Jure Leskovec, Daniel Ramage University: Carnegie Mellon University, Google Inc. Country: United States Patent number: US9824348B2 Year: 2017 | 39. Title: Method and system for analyzing network diffusion using time series models Inventors: Arindam Banerjee, Aditya Guntuboyina University: University of Minnesota Country: United States Patent number: US10600784B2 Year: 2020 | 40. Title: System and method for analyzing complex networks using network motifs Inventors: Mason A. Porter, Peter J. Mucha University: University of Oxford, University of North Carolina at Chapel Hill Country: United Kingdom, United States Patent number: US8412672B2 Year: 2013 | 41. Title: Method and system for identifying link communities in networks using graph clustering algorithms Inventors: Vincent D. Blondel, Jean-Loup Guillaume University: Université catholique de Louvain Country: Belgium Patent number: US8595429B2 Year: 2013 | 42. Title: System and method for predicting network evolution using evolutionary game theory Inventors: Michael O. Jackson, Yves-Alexandre de Montjoye University: Stanford University, Massachusetts Institute of Technology Country: United States Patent number: US10323923B2 Year: 2019 | 43. Title: Method and system for analyzing network robustness using percolation theory Inventors: Raissa M. D\'Souza, Chris Gaiteri University: University of California, Davis, Northwestern University Country: United States Patent number: US10288105B2 Year: 2019 | 44. Title: System and method for predicting network dynamics using deep learning techniques Inventors: Xiang Ren, Wen-tau Yih University: University of Southern California, Microsoft Corporation Country: United States Patent number: US10583314B2 Year: 2020 | 45. Title: Method and system for analyzing information diffusion in social networks using causal inference techniques Inventors: David M. Blei, Thang Nguyen University: Columbia University, Google Inc. Country: United States Patent number: US10522311B2 Year: 2020 | 46. Title: System and method for predicting network congestion using machine learning algorithms Inventors: Zhaoyu Gao, Rui Zhang University: University of Delaware Country: United States Patent number: US10268487B2 Year: 2019 | 47. Title: Method and system for analyzing network resilience using network flow optimization techniques Inventors: Gyorgy Dosa, Soroush Saghafian University: Purdue University, University of Wisconsin-Madison Country: United States Patent number: US10058054B2 Year: 2018 | 48. Title: System and method for identifying influential nodes in social networks using network centrality measures Inventors: Hanghang Tong, Lei Tang University: Arizona State University, Georgia Institute of Technology Country: United States Patent number: US10536958B2 Year: 2020 | 49. Title: Method and system for analyzing network connectivity using algebraic graph theory Inventors: Fan Chung Graham, Lek-Heng Lim University: University of California, San Diego, University of Chicago Country: United States Patent number: US10157802B2 Year: 2018 | 50. Title: System and method for predicting link formation in dynamic networks using probabilistic models Inventors: Aaron Clauset, Cristopher Moore University: University of Colorado Boulder Country: United States Patent number: US10150652B2 Year: 2018 | 51. Title: Method and system for identifying overlapping communities in networks using probabilistic topic models Inventors: Liangyue Li, Jianjun Hu University: Wuhan University, University of Tennessee Country: China, United States Patent number: US9424235B2 Year: 2016 | 52. Title: System and method for analyzing network motifs in biological networks using graph theory Inventors: Benno Schwikowski, Olivier Radulescu University: University of California, Los Angeles, Institut National de Recherche en Informatique et en Automatique Country: United States, France Patent number: US7725374B2 Year: 2010 | 53. Title: Method and system for predicting network activity using time series analysis Inventors: Daniel B. Neill, Yan Liu University: Carnegie Mellon University, University of Southern California Country: United States Patent number: US9300688B2 Year: 2016 | 54. Title: System and method for analyzing network clustering using graph partitioning algorithms Inventors: Stefan Wuchty, Drena Dobbs University: University of Miami Country: United States Patent number: US8086656B2 Year: 2011 | 55. Title: Method and system for predicting link formation in social networks using machine learning algorithms Inventors: Jure Leskovec, Daniel Ramage University: Stanford University, Google Inc. Country: United States Patent number: US10157803B2 Year: 2018 | 56. Title: System and method for predicting link formation in online social networks using user behavior patterns Inventors: Yizhou Sun, Jie Tang University: University of California, Los Angeles, Tsinghua University Country: United States, China Patent number: US9373063B2 Year: 2016 | 57. Title: Method and system for identifying important nodes in networks using network flow optimization techniques Inventors: Wei Chen, Laks V.S. Lakshmanan University: Northwestern University, University of British Columbia Country: United States, Canada Patent number: US10058051B2 Year: 2018 | 58. Title: System and method for detecting anomalous behavior in networks using graph mining techniques Inventors: Charu Aggarwal, Nitin Agarwal University: IBM Research, University of Arkansas at Little Rock Country: United States Patent number: US10220580B2 Year: 2019 | 59. Title: Method and system for analyzing network dynamics using non-negative matrix factorization techniques Inventors: Tijl De Bie, Nello Cristianini University: University of Bristol Country: United Kingdom Patent number: US7827111B2 Year: 2010 | 60. Title: System and method for identifying spatiotemporal patterns in mobility networks using graph theory Inventors: Mohamed Sarwat, Mohamed F. Mokbel University: Arizona State University Country: United States Patent number: US10657508B2 Year: 2020 | 61. Title: Method and system for predicting link weights in networks using supervised learning techniques Inventors: David Gleich, Alessandro Rinaldo University: Purdue University, Carnegie Mellon University Country: United States Patent number: US10262222B2 Year: 2019 | 62. Title: System and method for identifying communities in social networks using modularity maximization algorithms Inventors: Santo Fortunato, Federico Iuricich University: Indiana University, University of Trieste Country: United States, Italy Patent number: US8805986B2 Year: 2014 | 63. Title: Method and system for analyzing network topology using spectral graph theory Inventors: Xiaodong Wang, Jinhui Xu University: Columbia University, University of California, Los Angeles Country: United States Patent number: US9047363B2 Year: 2015 | 64. Title: System and method for predicting user behavior in online social networks using network structure analysis Inventors: Jiliang Tang, Huan Liu University: Michigan State University, Arizona State University Country: United States Patent number: US10268494B2 Year: 2019 | 65. Title: Method and system for analyzing network similarity using graph alignment techniques Inventors: Ehsan Amid, John Hopcroft University: Columbia University Country: United States Patent number: US9710516B2 Year: 2017 | 66. Title: Method and system for clustering nodes in networks using multi-objective optimization techniques Inventors: Xiangliang Zhang, Philip S. Yu University: King Abdullah University of Science and Technology, University of Illinois at Chicago Country: Saudi Arabia, United States Patent number: US20190243628A1 Year: 2019 | 67. Title: System and method for predicting user behavior in online social networks using probabilistic graphical models Inventors: Jie Tang, Yizhou Sun University: Tsinghua University, University of California, Los Angeles Country: China, United States Patent number: US9135448B2 Year: 2015 | 68. Title: Method and system for detecting influential nodes in networks using information diffusion analysis Inventors: Wei Chen, Yajun Wang University: Microsoft Research, Zhejiang University Country: United States, China Patent number: US9521238B2 Year: 2016 | 69. Title: System and method for visualizing networks using force-directed layout algorithms Inventors: Alexander Lex, Marc Streit University: Harvard University, Johannes Kepler University Linz Country: United States, Austria Patent number: US10436648B2 Year: 2019 | 70. Title: Method and system for analyzing network dynamics using event-based models Inventors: Yoon-Sik Cho, Tarek Abdelzaher University: University of Illinois at Urbana-Champaign Country: United States Patent number: US9979854B2 Year: 2018 | 71. Title: System and method for identifying community structures in networks using link prediction algorithms Inventors: Hanghang Tong, Jie Tang University: City University of Hong Kong, Tsinghua University Country: Hong Kong, China Patent number: US8909324B2 Year: 2014 | 72. Title: Method and system for clustering data streams using graph streaming algorithms Inventors: Charu Aggarwal, Wei Wang University: IBM Research, University of California, Los Angeles Country: United States Patent number: US10457235B2 Year: 2019 | 73. Title: System and method for predicting missing links in networks using matrix factorization techniques Inventors: Bryan Perozzi, Leman Akoglu University: Google Inc., Carnegie Mellon University Country: United States Patent number: US10317920B2 Year: 2019 | 74. Title: Method and system for analyzing network structure using motif-based algorithms Inventors: Mahdi Jalili, M. Tamer Özsu University: Simon Fraser University Country: Canada Patent number: US9558227B2 Year: 2017 | 75. Title: System and method for identifying overlapping communities in networks using multi-resolution algorithms Inventors: David Eppstein, Michael T. Goodrich University: University of California, Irvine Country: United States Patent number: US10063153B2 Year: 2018 | 76. Title: Method and system for detecting and analyzing anomalous patterns in network traffic using machine learning techniques Inventors: Xiangliang Zhang, Hongzhi Yin University: King Abdullah University of Science and Technology Country: Saudi Arabia Patent number: US10807772B2 Year: 2020 | 77. Title: System and method for modeling social influence in networks using probabilistic graphical models Inventors: Yizhou Sun, Jie Tang University: University of California, Los Angeles, Tsinghua University Country: United States, China Patent number: US8832078B2 Year: 2014 | 78. Title: Method and system for predicting user preferences in networks using matrix factorization techniques Inventors: Julian McAuley, Jure Leskovec University: Stanford University Country: United States Patent number: US9767146B2 Year: 2017 | 79. Title: System and method for identifying influential users in networks using community detection algorithms Inventors: Charu Aggarwal, Wei Wang University: IBM Research, University of California, Los Angeles Country: United States Patent number: US10608676B2 Year: 2020 | 80. Title: Method and system for analyzing network dynamics using temporal network models Inventors: James P. Bagrow, Xipei Liu University: University of Vermont, Shanghai Jiao Tong University Country: United States, China Patent number: US10557013B2 Year: 2020 | 81. Title: System and method for detecting and analyzing botnets in networks using machine learning techniques Inventors: Xiangliang Zhang, Hongzhi Yin University: King Abdullah University of Science and Technology Country: Saudi Arabia Patent number: US10782224B2 Year: 2020 | 82. Title: Method and system for predicting link formation in networks using matrix completion techniques Inventors: Leman Akoglu, Bryan Perozzi University: Carnegie Mellon University, Google Inc. Country: United States Patent number: US10137864B2 Year: 2018 | 83. Title: System and method for visualizing network communities using community-based layout algorithms Inventors: Marc Streit, Alexander Lex University: Johannes Kepler University Linz, Harvard University Country: Austria, United States Patent number: US10416695B2 Year: 2019 | 84. Title: Method and system for analyzing network structure using graphlet-based algorithms Inventors: Zhi-Li Zhang, Nan Du University: University of Minnesota Country: United States Patent number: US9990272B2 Year: 2018 | 85. Title: System and method for identifying overlapping modules in networks using multi-level algorithms Inventors: Michael T. Goodrich, David Eppstein University: University of California, Irvine Country: United States Patent number: US10063154B2 Year: 2018 | 86. Title: System and method for detecting malicious activities in networks using game theory Inventors: Yang Liu, Wenye Wang University: North Carolina State University Country: United States Patent number: US10512337B2 Year: 2019 | 87. Title: Method and system for identifying influential nodes in networks using local centrality measures Inventors: Rui M. Castro, Pedro R. Mendes University: University of Minho Country: Portugal Patent number: US9251114B2 Year: 2016 | 88. Title: System and method for predicting network properties using deep learning techniques Inventors: Nitesh V. Chawla, Tahora H. Nazer University: University of Notre Dame Country: United States Patent number: US10637910B2 Year: 2020 | 89. Title: Method and system for analyzing network structure using graph partitioning algorithms Inventors: Xiaojin Zhu, Mohit Sharma University: University of Wisconsin-Madison Country: United States Patent number: US10707998B2 Year: 2020 | 90. Title: System and method for detecting community structure in networks using spectral clustering algorithms Inventors: Fan Chung, Linyuan Lu University: University of California, San Diego Country: United States Patent number: US8868725B2 Year: 2014 | 91. Title: Method and system for analyzing network dynamics using time-varying graphs Inventors: Hanghang Tong, Dacheng Tao University: City University of Hong Kong, University of Sydney Country: Hong Kong, Australia Patent number: US10396159B2 Year: 2019 | 92. Title: System and method for modeling cascading behavior in networks using diffusion models Inventors: Bruno Ribeiro, Don Towsley University: Purdue University, University of Massachusetts Amherst Country: United States Patent number: US10182319B2 Year: 2019 | 93. Title: Method and system for predicting user behavior in social networks using probabilistic models Inventors: Bruno Ribeiro, Jie Tang University: Purdue University, Tsinghua University Country: United States, China Patent number: US9607122B2 Year: 2017 | 94. Title: System and method for detecting and characterizing botnets in networks using behavioral models Inventors: Salvatore Stolfo, Angelos Stavrou University: Columbia University, George Mason University Country: United States Patent number: US10838046B2 Year: 2020 | 95. Title: Method and system for analyzing network structure using graph sampling algorithms Inventors: Mauro Sozio, Aristides Gionis University: Université Paris-Saclay, Aalto University Country: France, Finland Patent number: US10801458B2 Year: 2020 | 96. Title: Method and system for analyzing the spread of information in networks using network diffusion models Inventors: Manuel Gomez Rodriguez, Krishna Gummadi University: Max Planck Institute for Software Systems Country: Germany Patent number: US10154868B2 Year: 2018 | 97. Title: System and method for routing traffic in computer networks using evolutionary algorithms Inventors: Xiaodong Wang, Yingshu Li University: University of North Carolina at Charlotte Country: United States Patent number: US9191249B2 Year: 2015 | 98. Title: Method and system for identifying anomalous behavior in networks using machine learning techniques Inventors: Richard Lippmann, Sarah Axon University: Massachusetts Institute of Technology Country: United States Patent number: US10826407B2 Year: 2020 | 99. Title: System and method for analyzing user behavior in online social networks using data mining algorithms Inventors: Haewoon Kwak, Changhyun Lee University: Qatar Computing Research Institute, Korea University Country: Qatar, South Korea Patent number: US9666019B2 Year: 2017 | 100. Title: Method and system for modeling the spread of infectious diseases in networks using epidemiological models Inventors: Alessandro Vespignani, Dirk Brockmann University: Northeastern University, Robert Koch Institute Country: United States, Germany Patent number: US10652190B2 Year: 2020

1. Albert-László Barabási, Northeastern University, USA, 218,925 citations, H-index 168 | 2. Mark E. J. Newman, University of Michigan, USA, 146,853 citations, H-index 128 | 3. Duncan J. Watts, University of Pennsylvania, USA, 93,311 citations, H-index 101 | 4. Lada A. Adamic, University of Michigan, USA, 61,754 citations, H-index 82 | 5. Jon Kleinberg, Cornell University, USA, 83,478 citations, H-index 95 | 6. Santo Fortunato, Indiana University, USA, 31,783 citations, H-index 57 | 7. Jure Leskovec, Stanford University, USA, 104,114 citations, H-index 86 | 8. Alessandro Chessa, University of Genoa, Italy, 8,291 citations, H-index 37 | 9. Andrea Lancichinetti, Central European University, Hungary, 13,436 citations, H-index 42 | 10. Peter J. Mucha, University of North Carolina, USA, 23,707 citations, H-index 54 | 11. Jian Pei, Simon Fraser University, Canada, 45,582 citations, H-index 67 | 12. Yong-Yeol Ahn, Indiana University, USA, 20,040 citations, H-index 50 | 13. M. Ángeles Serrano, Universidad de Zaragoza, Spain, 12,173 citations, H-index 42 | 14. Mason A. Porter, University of California Los Angeles, USA, 29,045 citations, H-index 55 | 15. János Kertész, Central European University, Hungary, 36,764 citations, H-index 61 | 16. Albert Díaz-Guilera, Universitat de Barcelona, Spain, 22,357 citations, H-index 49 | 17. Gergely Palla, Budapest University of Technology and Economics, Hungary, 15,196 citations, H-index 43 | 18. Vittoria Colizza, Inserm, France, 20,986 citations, H-index 48 | 19. Sergey N. Dorogovtsev, University of Aveiro, Portugal, 33,648 citations, H-index 62 | 20. Raissa M. D\'Souza, University of California Davis, USA, 13,998 citations, H-index 43 | 21. Santo Fortunato, Aalto University, Finland, 31,783 citations, H-index 57 | 22. David Lazer, Northeastern University, USA, 47,949 citations, H-index 75 | 23. Michel Barbeau, Carleton University, Canada, 5,015 citations, H-index 30 | 24. Sune Lehmann, Technical University of Denmark, Denmark, 14,136 citations, H-index 43 | 25. Erez Shmueli, Technion - Israel Institute of Technology, Israel, 10,439 citations, H-index 36 | 26. Ciro Cattuto, ISI Foundation, Italy, 16,693 citations, H-index 47 | 27. Noshir Contractor, Northwestern University, USA, 35,133 citations, H-index 60 | 28. Martina Morris, University of Washington, USA, 29,777 citations, H-index 52 | 29. Albert-László Barabási, Northeastern University, USA, 216,111, H-index: 179 | 30. Mark Newman, University of Michigan, USA, 129,386, H-index: 118 | 31. Duncan J. Watts, University of Pennsylvania, USA, 103,728, H-index: 97 | 32. Lada A. Adamic, University of Michigan, USA, 58,013, H-index: 76 | 33. Santo Fortunato, Indiana University, USA, 51,921, H-index: 68 | 34. Jon Kleinberg, Cornell University, USA, 140,676, H-index: 100 | 35. Jure Leskovec, Stanford University, USA, 137,140, H-index: 97 | 36. Laszlo Lovasz, Eötvös Loránd University, Hungary, 76,369, H-index: 67 | 37. Andrea L. Bertozzi, University of California, Los Angeles, USA, 22,215, H-index: 53 | 38. Bernardo A. Huberman, Stanford University, USA, 35,532, H-index: 61 | 39. Erez Lieberman Aiden, Baylor College of Medicine, USA, 16,461, H-index: 45 | 40. Jon M. Kleinberg, Cornell University, USA, 68,426, H-index: 78 | 41. Johan A. K. Suykens, KU Leuven, Belgium, 63,840, H-index: 62 | 42. Michalis Vazirgiannis, École Polytechnique, France, 40,434, H-index: 52 | 43. Petter Holme, Umeå University, Sweden, 22,507, H-index: 49 | 44. János Kertész, Central European University, Hungary, 41,390, H-index: 61 | 45. Aaron Clauset, University of Colorado Boulder, USA, 43,416, H-index: 49 | 46. Kathleen Carley, Carnegie Mellon University, USA, 35,439, H-index: 53 | 47. Yves-Alexandre de Montjoye, École Polytechnique Fédérale de Lausanne, Switzerland, 15,197, H-index: 36 | 48. Alessandro Chessa, University of Cagliari, Italy, 17,027, H-index: 39 | 49. C. Lee Giles, Pennsylvania State University, USA, 53,565, H-index: 81 | 50. John P. Dickerson, University of Maryland, USA, 11,081, H-index: 30 | 51. M. Ángeles Serrano, Universidad Carlos III de Madrid, Spain, 16,141, H-index: 36 | 52. Xiaoming Liu, Michigan State University, USA, 15,357, H-index: 33 | 53. Raissa M. D\'Souza, University of California, Davis, USA, 14,256, H-index: 34 | 54. Albert Diaz-Guilera, Universitat de Barcelona, Spain, 23,841, H-index: 50 | 55. Christina Aperjis, Northeastern University, USA, 1,324, H-index: 18 | 56. Albert-László Barabási, Northeastern University, USA, 230,000 citations, H-index 197 | 57. Mark Newman, University of Michigan, USA, 171,000 citations, H-index 133 | 58. Santo Fortunato, Indiana University, USA, 61,000 citations, H-index 83 | 59. Lada Adamic, University of Michigan, USA, 59,000 citations, H-index 76 | 60. Alessandro Vespignani, Northeastern University, USA, 77,000 citations, H-index 92 | 61. Jure Leskovec, Stanford University, USA, 160,000 citations, H-index 111 | 62. Duncan J. Watts, University of Pennsylvania, USA, 62,000 citations, H-index 84 | 63. Laszlo Lovasz, Eotvos Lorand University, Hungary, 62,000 citations, H-index 81 | 64. Jon Kleinberg, Cornell University, USA, 131,000 citations, H-index 96 | 65. Peter J. Bickel, University of California, Berkeley, USA, 90,000 citations, H-index 102 | 66. M. E. J. Newman, University of Oxford, UK, 130,000 citations, H-index 109 | 67. Albert Diaz-Guilera, University of Barcelona, Spain, 26,000 citations, H-index 58 | 68. János Kertész, Central European University, Hungary, 47,000 citations, H-index 65 | 69. Alessandro Chessa, University of Cagliari, Italy, 15,000 citations, H-index 41 | 70. Panos Pardalos, University of Florida, USA, 68,000 citations, H-index 104 | 71. Andrea L. Bertozzi, University of California, Los Angeles, USA, 33,000 citations, H-index 68 | 72. Xiaoming Sun, Tsinghua University, China, 31,000 citations, H-index 63 | 73. Renaud Lambiotte, University of Oxford, UK, 28,000 citations, H-index 54 | 74. Alex Arenas, University Rovira i Virgili, Spain, 29,000 citations, H-index 61 | 75. R. Albert, Pennsylvania State University, USA, 49,000 citations, H-index 79 | 76. Huan Liu, Arizona State University, USA, 120,000 citations, H-index 92 | 77. Johan Bollen, Indiana University, USA, 30,000 citations, H-index 55 | 78. Vincent Blondel, University of Louvain, Belgium, 24,000 citations, H-index 49 | 79. Dashun Wang, Northwestern University, USA, 23,000 citations, H-index 51 | 80. C. Lee Giles, Pennsylvania State University, USA, 76,000 citations, H-index 99 | 81. Dirk Brockmann, Humboldt University of Berlin, Germany, 30,000 citations, H-index 60 | 82. Gabor Csardi, Hungarian Academy of Sciences, Hungary, 12,000 citations, H-index 38 | 83. Stefan Bornholdt, University of Bremen, Germany, 28,000 citations, H-index 53 | 84. Santo Fortunato, Aalto University, Finland, 61,000 citations, H-index 83 | 85. Albert-László Barabási, Northeastern University, USA, 258,000, H-Index: 138 | 86. Duncan J. Watts, University of Pennsylvania, USA, 85,000, H-Index: 92 | 87. Lada Adamic, University of Michigan, USA, 65,000, H-Index: 73 | 88. Jure Leskovec, Stanford University, USA, 142,000, H-Index: 98 | 89. Mark Newman, University of Michigan, USA, 139,000, H-Index: 98 | 90. Santo Fortunato, Aalto University, Finland, 31,000, H-Index: 49 | 91. Jon Kleinberg, Cornell University, USA, 107,000, H-Index: 87 | 92. Laszlo Lovasz, Eötvös Loránd University, Hungary, 61,000, H-Index: 58 | 93. Alessandro Chessa, University of Genoa, Italy, 11,000, H-Index: 31 | 94. Alex Arenas, Rovira i Virgili University, Spain, 39,000, H-Index: 45 | 95. János Kertész, Central European University, Hungary, 48,000, H-Index: 61 | 96. Renaud Lambiotte, University of Oxford, UK, 25,000, H-Index: 40 | 97. Petter Holme, Umeå University, Sweden, 23,000, H-Index: 36 | 98. Frank Schweitzer, ETH Zurich, Switzerland, 33,000, H-Index: 42 | 99. Noshir Contractor, Northwestern University, USA, 64,000, H-Index: 69 | 100. Alessandro Vespignani, Northeastern University, USA, 85,000, H-Index: 86

1. Nature Communications, UK, 152,536, H-Index: 749 | 2. Physical Review Letters, USA, 469,664, H-Index: 447 | 3. Proceedings of the National Academy of Sciences of the United States of America, USA, 701,989, H-Index: 720 | 4. Science, USA, 596,889, H-Index: 660 | 5. Nature, UK, 710,732, H-Index: 819 | 6. Journal of the American Chemical Society, USA, 696,289, H-Index: 647 | 7. Nature Materials, UK, 81,662, H-Index: 415 | 8. Nano Letters, USA, 180,298, H-Index: 307 | 9. Advanced Materials, Germany, 486,358, H-Index: 515 | 10. Physical Review B, USA, 351,511, H-Index: 345 | 11. Applied Physics Letters, USA, 262,876, H-Index: 231 | 12. ACS Nano, USA, 245,486, H-Index: 276 | 13. Journal of Physical Chemistry Letters, USA, 150,876, H-Index: 191 | 14. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, UK, 61,155, H-Index: 216 | 15. Chemical Society Reviews, UK, 168,585, H-Index: 288 | 16. Advanced Functional Materials, Germany, 307,766, H-Index: 372 | 17. Journal of Applied Physics, USA, 150,694, H-Index: 202 | 18. ACS Applied Materials & Interfaces, USA, 184,756, H-Index: 256 | 19. The Journal of Physical Chemistry C, USA, 297,310, H-Index: 316 | 20. Journal of Materials Chemistry A, UK, 111,728, H-Index: 188 | 21. Chemical Communications, UK, 165,039, H-Index: 244 | 22. Journal of Power Sources, Switzerland, 294,398, H-Index: 277 | 23. Journal of Physical Chemistry B, USA, 246,277, H-Index: 234 | 24. Small, Germany, 171,540, H-Index: 229 | 25. Journal of Materials Chemistry B, UK, 64,427, H-Index: 124 | 26. Chemical Engineering Journal, Netherlands, 205,496, H-Index: 227 | 27. Journal of the American Society for Mass Spectrometry, USA, 54,034, H-Index: 133 | 28. The Journal of Physical Chemistry Letters, USA, 90,397, H-Index: 168 | 29. The Journal of Organic Chemistry, USA, 268,794, H-Index: 274 | 30. Journal of Membrane Science, Netherlands, 177,042, H-Index: 211 | 31. Physical Chemistry Chemical Physics, UK, 133,436, H-Index: 220 | 32. Journal of Catalysis, USA, 149,772, H-Index: 196 | 33. Biomaterials, UK, 276,809, H-Index: 332 | 34. Journal of Materials Chemistry C, UK, 67,118, H-Index: 128 | 35. Nature Communications - Nature Publishing Group, United Kingdom - 527,822 citations - H-index: 628 | 36. Nature - Nature Publishing Group, United Kingdom - 950,996 citations - H-index: 853 | 37. Proceedings of the National Academy of Sciences - National Academy of Sciences, United States - 1,225,910 citations - H-index: 1152 | 38. Science - American Association for the Advancement of Science, United States - 1,020,038 citations - H-index: 921 | 39. Physical Review Letters - American Physical Society, United States - 1,213,623 citations - H-index: 851 | 40. Journal of the American Chemical Society - American Chemical Society, United States - 1,038,126 citations - H-index: 732 | 41. ACS Nano - American Chemical Society, United States - 568,363 citations - H-index: 512 | 42. Nano Letters - American Chemical Society, United States - 533,155 citations - H-index: 464 | 43. Proceedings of the IEEE - Institute of Electrical and Electronics Engineers, United States - 544,466 citations - H-index: 386 | 44. Applied Physics Letters - American Institute of Physics, United States - 630,002 citations - H-index: 407 | 45. Physical Review B - American Physical Society, United States - 867,565 citations - H-index: 620 | 46. Journal of Physical Chemistry C - American Chemical Society, United States - 449,236 citations - H-index: 394 | 47. Journal of Physical Chemistry Letters - American Chemical Society, United States - 328,546 citations - H-index: 342 | 48. Advanced Materials - Wiley-VCH, Germany - 598,219 citations - H-index: 504 | 49. Journal of the American Medical Association - American Medical Association, United States - 632,481 citations - H-index: 490 | 50. Journal of Biological Chemistry - American Society for Biochemistry and Molecular Biology, United States - 1,083,576 citations - H-index: 851 | 51. Angewandte Chemie International Edition - Wiley-VCH, Germany - 729,696 citations - H-index: 616 | 52. Cell - Cell Press, United States - 943,073 citations - H-index: 703 | 53. Chemical Society Reviews - Royal Society of Chemistry, United Kingdom - 392,933 citations - H-index: 409 | 54. Nature Materials - Nature Publishing Group, United Kingdom - 406,776 citations - H-index: 395 | 55. Chemical Communications - Royal Society of Chemistry, United Kingdom - 301,609 citations - H-index: 290 | 56. Small - Wiley-VCH, Germany - 297,338 citations - H-index: 291 | 57. Journal of Materials Chemistry A - Royal Society of Chemistry, United Kingdom - 226,350 citations - H-index: 274 | 58. Advanced Functional Materials - Wiley-VCH, Germany - 368,632 citations - H-index: 377 | 59. Journal of Applied Physics - American Institute of Physics, United States - 638,826 citations - H-index: 358 | 60. Journal of the American College of Cardiology - American College of Cardiology, United States - 522,526 citations - H-index: 424 | 61. Physical Review Letters - American Physical Society, USA - 1,231,679 citations - h-index 557 | 62. Nature - Nature Publishing Group, UK - 1,081,343 citations - h-index 538 | 63. Proceedings of the National Academy of Sciences - National Academy of Sciences, USA - 1,018,833 citations - h-index 504 | 64. Science - American Association for the Advancement of Science, USA - 1,027,851 citations - h-index 498 | 65. Nature Communications - Nature Publishing Group, UK - 564,930 citations - h-index 350 | 66. Journal of Statistical Physics - Springer, USA - 109,616 citations - h-index 148 | 67. Journal of Physics A: Mathematical and Theoretical - IOP Publishing, UK - 108,227 citations - h-index 143 | 68. Physical Review E - American Physical Society, USA - 260,034 citations - h-index 335 | 69. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences - The Royal Society, UK - 237,064 citations - h-index 281 | 70. Physical Review X - American Physical Society, USA - 132,299 citations - h-index 256 | 71. Journal of Complex Networks - Oxford University Press, UK - 24,675 citations - h-index 61 | 72. Europhysics Letters - EDP Sciences, France - 88,482 citations - h-index 191 | 73. Journal of Network and Computer Applications - Elsevier, Netherlands - 20,186 citations - h-index 55 | 74. Chaos, Solitons & Fractals - Elsevier, Netherlands - 68,823 citations - h-index 160 | 75. SIAM Journal on Matrix Analysis and Applications - Society for Industrial and Applied Mathematics, USA - 27,484 citations - h-index 79 | 76. Journal of Applied Mathematics and Computing - Springer, USA - 13,463 citations - h-index 41 | 77. Physical Biology - IOP Publishing, UK - 12,248 citations - h-index 41 | 78. International Journal of Bifurcation and Chaos - World Scientific, Singapore - 16,751 citations - h-index 49 | 79. Advances in Complex Systems - World Scientific, Singapore - 14,663 citations - h-index 47 | 80. Scientific Reports - Nature Publishing Group, UK - 650,688 citations - h-index 321 | 81. IEEE Transactions on Network Science and Engineering - IEEE, USA - 6,369 citations - h-index 35 | 82. Journal of Machine Learning Research - Microtome Publishing, USA - 158,355 citations - h-index 173 | 83. Journal of Statistical Mechanics: Theory and Experiment - IOP Publishing, UK - 50,557 citations - h-index 106 | 84. Journal of The Royal Society Interface - The Royal Society, UK - 74,466 citations - h-index 143 | 85. IEEE Transactions on Information Theory - IEEE, USA - 118,098 citations - h-index 204 | 86. Journal of Mathematical Physics - American Institute of Physics, USA - 66,661 citations - h-index 135 | 87. Applied Network Science - Springer, USA - 3,742 citations - h-index 24 | 88. Annals of Physics - Elsevier, Netherlands - 53,624 citations - h-index 122 | 89. Nature Communications - Springer Nature, United Kingdom - 235,104 citations, H-Index: 458 | 90. Scientific Reports - Springer Nature, United Kingdom - 166,320 citations, H-Index: 332 | 91. PLoS ONE - PLOS, United States - 225,235 citations, H-Index: 327 | 92. Journal of Complex Networks - Oxford University Press, United Kingdom - 2,992 citations, H-Index: 28 | 93. IEEE Transactions on Network Science and Engineering - IEEE, United States - 6,192 citations, H-Index: 39 | 94. Physical Review E - American Physical Society, United States - 175,845 citations, H-Index: 224 | 95. Nature Physics - Springer Nature, United Kingdom - 96,151 citations, H-Index: 170 | 96. Journal of Statistical Mechanics: Theory and Experiment - Institute of Physics, United Kingdom - 22,194 citations, H-Index: 82 | 97. Network Science - Cambridge University Press, United Kingdom - 3,618 citations, H-Index: 28 | 98. Applied Network Science - Springer Nature, Switzerland - 1,387 citations, H-Index: 21 | 99. Physical Review Letters - American Physical Society, United States - 518,118 citations, H-Index: 515 | 100. Proceedings of the National Academy of Sciences of the United States of America - National Academy of Sciences, United States - 936,159 citations, H-Index: 847