Research and Teaching Assistant at Izmir Institute of Technology, Turkey📖
Büşra Çalmaz is a dedicated Postdoctoral Research Fellow specializing in computer engineering with a focus on graph mining and k-clique counting algorithms. She has extensive experience in developing computational methodologies for analyzing large-scale networks, emphasizing applications in social network analysis, bioinformatics, and cybersecurity. Her innovative contributions include k-clique counting and frequent subgraph mining, techniques essential for uncovering complex patterns in extensive datasets. Büşra has published her work in high-impact journals and is committed to advancing graph-based methodologies through research and collaboration.
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Education Background🎓
Büşra is pursuing a Ph.D. in Computer Science at the Izmir Institute of Technology, Turkey, where her thesis focuses on “Graphlet Mining in Big Data” and is expected to be completed in 2024. She earned her M.Sc. in Computer Science from the same institution in 2018, with a thesis on analyzing social media data through frequent pattern mining methods. Her academic journey began with a B.Sc. in Computer Science from Selçuk University, Konya, Turkey, in 2015.
Professional Experience🌱
Since 2016, Büşra has served as a Research and Teaching Assistant in the Computer Engineering Department at the Izmir Institute of Technology. She contributes to various courses, including Data Structures, Computer Networks, and Distributed Information Management, and develops practical assignments and lab sessions to enhance learning. She also assists in grading, assessing student work, and collaborating with faculty to refine course content and teaching methodologies.
Büşra’s research interests lie in graph mining, frequent subgraph mining, k-clique counting algorithms, and large-scale network analysis. She is passionate about using computational methodologies to solve real-world problems in fields like social network analysis, biological motif identification, and cybersecurity. Her recent work emphasizes enhancing algorithmic efficiency and scalability for analyzing complex networks, with a strong inclination toward integrating advanced tools like Apache Spark and Hadoop for distributed computing.
Author Metrics
Publications Top Notes 📄
1. A Qualitative Survey on Frequent Subgraph Mining
- Authors: B. Güvenoglu, B.E. Bostanoğlu
- Published in: Open Computer Science, Volume 8, Issue 1, Pages 194-209
- Year: 2018
- DOI: https://doi.org/10.1515/comp-2018-0018
- Abstract: This paper provides a comprehensive survey on frequent subgraph mining techniques, detailing methodologies, algorithmic advancements, and applications across domains such as bioinformatics, social networks, and cheminformatics. The authors emphasize the computational challenges of scalability and propose qualitative comparisons to guide researchers in selecting appropriate methods.
- Citations: 15
2. BDAC: Boundary-Driven Approximations of K-Cliques
- Authors: B. Çalmaz, B.E. Bostanoğlu
- Published in: Symmetry, Volume 16, Issue 8, Article 983
- Year: 2024
- DOI: https://doi.org/10.3390/sym16080983
- Abstract: This paper introduces the BDAC algorithm, a novel approach to approximate k-clique counts in large-scale networks using boundary-driven techniques. The method balances accuracy and computational efficiency, making it suitable for real-world applications in large datasets. Performance is evaluated against state-of-the-art algorithms, showcasing improvements in both runtime and memory usage.
- Citations: 1
3. k-Clique Counting on Large Scale-Graphs: A Survey
- Authors: B. Çalmaz, B.E. Bostanoğlu
- Published in: PeerJ Computer Science, Volume 10, Article e2501
- Year: 2024
- Abstract: This survey explores the challenges and methodologies associated with k-clique counting in large-scale graphs, focusing on recent advancements in parallel computing and approximation algorithms. It highlights application areas, including community detection and fraud detection, and outlines open research challenges in the field.
- Status: In press / Awaiting publication
4. Analyzing Social Media Data by Frequent Pattern Mining Methods
- Author: B. Güvenoğlu
- Published in: PQDT-Global
- Year: 2018
- Abstract: This study applies frequent pattern mining techniques to analyze social media data, extracting insights about user behavior and interaction patterns. The research demonstrates the utility of data mining in handling unstructured data from platforms like Twitter and Facebook, with implications for marketing and sentiment analysis.
- Thesis publication: Based on M.Sc. thesis work
Conclusion
Mrs. Büşra Çalmaz is an outstanding candidate for the Best Researcher Award, with a strong portfolio of impactful publications and innovative contributions to graph mining and k-clique counting algorithms. Her dedication to solving complex computational problems, combined with her academic achievements and teaching experience, positions her as a leader in her field.
While she could enhance her research through industry collaborations and community engagement, her existing strengths make her highly deserving of this recognition. Awarding her would not only acknowledge her significant achievements but also encourage further groundbreaking work in graph mining and large-scale network analysis.