Professor, People’s Public Security University of China, China
Dr. Yijun Gu is a distinguished professor with a Ph.D. in Computer Science. His research is centered on Blockchain technology and Social Networks. He is currently affiliated with the Department of Computer Science and Engineering at the Beijing Institute of Technology and the College of Information and Cyber Security at the People’s Public Security University of China. 🌐📊 Dr. Gu is known for his innovative work in unsupervised community detection, social media rumor detection, and edge computing solutions.
Publication Profile
Scopus
Education
Dr. Gu earned his Ph.D. in Computer Science from the School of Information Science and Technology at the Beijing Institute of Technology in March 2005. 🎓 His academic journey has solidified his expertise in Blockchain, social networks, and cutting-edge data science.
Experience
Dr. Gu has been instrumental in advancing research at the Beijing Institute of Technology and the People’s Public Security University of China. 💻 As a professor, he has not only led innovative projects but also mentored a new generation of researchers in information security and data analysis. His expertise spans social network analysis, blockchain systems, and artificial intelligence applications.
Research Focus
Dr. Gu’s research primarily focuses on Blockchain technology, social network analysis, and advanced community detection algorithms. 🔗📡 He has also contributed significantly to vehicular edge computing, rumor detection models, and fraud detection systems through graph-based approaches. His work merges AI with cybersecurity and data-driven methodologies to solve real-world problems.
Awards and Honors
Dr. Gu has been recognized for his contributions to computer science and cybersecurity with numerous academic accolades. 🏆 His pioneering research in blockchain and rumor detection has gained international attention, solidifying his position as a leading figure in the field.
Publication Top Notes
Dr. Yijun Gu has authored several influential papers, including works on unsupervised community detection, multimodal rumor detection, and task offloading in vehicular edge computing. 📚 His research has been published in reputable journals like Applied Sciences, Data Analysis and Knowledge Discovery, and Journal of Frontiers of Computer Science & Technology.
Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity, Applied Sciences, 2023, has been widely cited for its novel approach to community detection. Cited by 12 articles
Hypergraph-Based Rumor Detection Model Integrating User Propagation Bias Information, Data Analysis and Knowledge Discovery, 2024, offers new insights into rumor detection. Cited by 8 articles
A Graph Convolutional Credit Fraud Detection Model with Spatial-Neighborhood Adaptability, Data Analysis and Knowledge Discovery, 2024, is noted for improving credit fraud detection techniques. Cited by 15 articles