Mingqiang Wu | Graph Neural Networks | Best Researcher Award

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Dr. Mingqiang Wu | Graph Neural Networks | Best Researcher Award

Hunan University of Arts and Science | China

Author Profile

Scopus

Academic Profile of Dr. Mingqiang Wu

Early Academic Pursuits

Dr. Mingqiang Wu’s academic journey reflects a strong foundation in computer science, built through consistent pursuit of excellence at multiple levels of higher education. He obtained his Bachelor of Engineering in Computer Science from Shangqiu University, where he developed his initial interest in computational models and algorithms. His academic drive led him to pursue a Master of Engineering in Computer Science at Northwest Minzu University, where he began exploring advanced computational frameworks. Furthering his expertise, Dr. Wu completed his Ph.D. in Computer Science at Hohai University, which provided him with a research-oriented mindset and equipped him to contribute significantly to emerging fields in artificial intelligence and data-driven technologies.

Professional Endeavors

Professionally, Dr. Wu has established himself as a researcher and thought leader in the field of graph neural networks and graph data structures. His role as Principal Investigator of the Graduate Research Innovation Project at Northwest Minzu University highlights his leadership in guiding academic inquiry, fostering innovation, and mentoring young researchers. His academic awards and recognition, including his inclusion in the Wiley China Excellent Author Program, underscore his influence and recognition in the research community.

Contributions and Research Focus

Dr. Wu’s research contributions are particularly significant in the area of graph-based machine learning, with a focus on semi-supervised short text classification. His publications in high-impact journals such as Expert Systems with Applications (Impact Factor 7.5, JCR Q1) and Applied Soft Computing (Impact Factor 6.6, JCR Q1) demonstrate both quality and impact. His work on commonsense knowledge-powered heterogeneous graph attention networks, heterogeneous graph contrastive learning, and multi-source graph contrastive learning represent innovative approaches that integrate adaptive data augmentation and dual-level dynamic fusion strategies. These contributions provide scalable and intelligent solutions for real-world applications in natural language processing, knowledge engineering, and AI systems.

Impact and Influence

The impact of Dr. Wu’s work is evident not only in the high citation potential of his Q1 and Q2 publications but also in the way his models address practical challenges in AI-driven text analysis. His contributions strengthen the research ecosystem in graph neural networks, and his methods offer significant advancements for industries relying on intelligent text classification, such as information retrieval, recommendation systems, and semantic analysis. His recognition through awards and academic programs further solidifies his influence in shaping the direction of computational research.

Academic Citations and Recognition

With multiple publications in top-ranked journals, Dr. Wu’s work holds a strong presence in the academic community. His research papers, published in journals with impact factors ranging from 2.3 to 7.5, stand as benchmarks in their respective subdomains. The growing academic citations of his work highlight its value to the global research community and establish his contributions as authoritative references for future studies in graph-based AI systems.

Legacy and Future Contributions

Dr. Wu’s legacy lies in his ability to bridge theoretical foundations with practical advancements in computer science. His focus on semi-supervised learning, graph attention mechanisms, and adaptive algorithms continues to inspire young researchers and practitioners. Looking ahead, his future contributions are likely to extend into next-generation graph neural networks, scalable AI models, and interdisciplinary applications, thereby enhancing both academic discourse and industrial applications.

Conclusion

In conclusion, Dr. Mingqiang Wu exemplifies the qualities of an outstanding academic and researcher, with a strong record of achievements in graph data structures and algorithms. His journey from rigorous academic training to impactful research output demonstrates both commitment and innovation. With high-impact publications, leadership in academic projects, and recognition at both national and international levels, Dr. Wu is poised to continue making substantial contributions to the advancement of computer science and artificial intelligence.

Notable Publications

"Multi-source graph contrastive learning with dual-level dynamic fusion of structure and feature for inductive semi-supervised short text classification" 

"Heterogeneous graph contrastive learning with adaptive data augmentation for semi-supervised short text classification" 

  • Author: Mingqiang Wu, Zhuoming Xu, Lei Zheng
  • Journal: Expert Syst. J. Knowl
  • Year: 2025

"Commonsense knowledge powered heterogeneous graph attention networks for semi-supervised short text classification" 

  • Author: Mingqiang Wu
  • Journal: Expert Syst. Appl
  • Year: 2023

 

Dongdong An | Graph Neural Networks | Best Researcher Award

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Assist. Prof. Dr. Dongdong An | Graph Neural Networks | Best Researcher Award

Lecture at Shanghai Normal University, China📖

Dr. AN Dongdong is a lecturer at Shanghai Normal University in the College of Information and Mechanical & Electrical Engineering. He has a strong academic background with a focus on the security and verification of AI and cyber-physical systems. His work, including research on Graph Neural Networks and dynamic verification, has contributed significantly to advancing the reliability and security of AI applications. Dr. An is also actively involved in several research projects funded by prestigious institutions like the National Natural Science Foundation of China.

Profile

Scopus Profile

Orcid Profile

Education Background🎓

  1. Ph.D. in Software Engineering (2013–2020), East China Normal University
    Supervisor: Prof. Jing Liu
  2. Master’s Program (2016–2018), French National Institute for Research in Computer Science and Automation (INRIA), Joint Training with Robert de Simone
  3. Bachelor’s in Software Engineering (2009–2013), East China Normal University

Professional Experience🌱

  1. Lecturer (2020–Present), Shanghai Normal University, College of Information and Mechanical & Electrical Engineering
  2. Researcher (2016–2018), INRIA, France, with Robert de Simone on advanced security modeling and verification techniques in AI
  3. Ph.D. Candidate (2013–2020), East China Normal University, School of Software Engineering, under the supervision of Prof. Jing Liu
Research Interests🔬
  • Verifiable and Efficient Security Training for Graph Neural Networks
  • Security Modeling and Verification of Trustworthy AI Systems
  • Uncertainty Modeling and Dynamic Verification for Cyber-Physical-Social Systems

Author Metrics

1. Total Publications: 6 (including journal and conference papers)

2. Notable Publications:

  • Dongdong An, Zongxu Pan, Xin Gao et al., stohMCharts: A Modeling Framework for Quantitative Performance Evaluation of Cyber-Physical-Social Systems, IEEE Access, 2023.
  • Dongdong An, Jing Liu, Xiaohong Chen, Haiying Sun, Formal modeling and dynamic verification for human cyber-physical systems under uncertain environment, Journal of Software, 2021.
  • Dongdong An, Jing Liu*, Min Zhang, et al., Uncertainty modeling and runtime verification for autonomous vehicles driving control, Journal of Systems and Software, 2020.

Dr. An’s work is widely recognized for its contributions to AI system security, with a particular focus on improving system verification under uncertainty, and developing more robust AI models for real-world applications.

Publications Top Notes 📄

1. TaneNet: Two-Level Attention Network Based on Emojis for Sentiment Analysis

  • Authors: Zhao, Q., Wu, P., Lian, J., An, D., Li, M.
  • Journal: IEEE Access
  • Year: 2024
  • Volume: 12
  • Pages: 86106–86119
  • Citations: 0

2. Louvain-Based Fusion of Topology and Attribute Structure of Social Networks

  • Authors: Zhao, Q., Miao, Y., Lian, J., Li, X., An, D.
  • Journal: Computing and Informatics
  • Year: 2024
  • Volume: 43(1)
  • Pages: 94–125
  • Citations: 0

3. HGNN-QSSA: Heterogeneous Graph Neural Networks With Quantitative Sampling and Structure-Aware Attention

  • Authors: Zhao, Q., Miao, Y., An, D., Lian, J., Li, M.
  • Journal: IEEE Access
  • Year: 2024
  • Volume: 12
  • Pages: 25512–25524
  • Citations: 1

4. Modeling Structured Dependency Tree with Graph Convolutional Networks for Aspect-Level Sentiment Classification

  • Authors: Zhao, Q., Yang, F., An, D., Lian, J.
  • Journal: Sensors
  • Year: 2024
  • Volume: 24(2)
  • Article Number: 418
  • Citations: 12

5. Sentiment Analysis Based on Heterogeneous Multi-Relation Signed Network

  • Authors: Zhao, Q., Yu, C., Huang, J., Lian, J., An, D.
  • Journal: Mathematics
  • Year: 2024
  • Volume: 12(2)
  • Article Number: 331
  • Citations: 2

Conclusion

Dr. Dongdong An is a highly deserving candidate for the Best Researcher Award due to his innovative contributions to AI security, particularly in the areas of Graph Neural Networks, uncertainty modeling, and dynamic verification. His academic credentials, research publications, and involvement in high-impact research projects make him a prominent figure in his field. With improvements in citation outreach, interdisciplinary collaboration, and practical applications, Dr. An has the potential to make even greater strides in the research community, further enhancing the trustworthiness and security of AI systems globally.

Final Recommendation:

Dr. Dongdong An’s pioneering work in the security of AI systems and Graph Neural Networks places him at the forefront of AI research. His commitment to improving the reliability and security of AI models makes him a worthy candidate for the Best Researcher Award.