学术报告|图神经网络异常检测再思考 & 人工智能在图计算问题上的应用

Abstract: With the rapid development of machine learning and deep learning models, there is increasing interest in applying these AI methods to graph computational problems. In this talk, we will introduce some representative work, including some of our recent works, for several algorithmic problem on graph data, such as shortest path queries, subgraph matching, subgraph counting, and querying noisy graphs. We will also outline other promising problems where AI methods may help and list open problems.

Short Biography: Dr. Wei Wang is a currently a Professor in the Data Science and Analytics Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), China. Before that, he was a Professor in the School of Computer Science and Engineering, The University of New South Wales, Australia. His current research interests include Similarity Query Processing, Artificial Intelligence, Knowledge Graphs, Security for AI Models, and AI for Science. He has published more than 160 papers in reputed journals and conferences, and has won the Best Paper Awards in SIGCOMM 2022, ICMR 2021, and the Best Student Paper at DASFAA 2016. He is an Associate Editor of IEEE Transactions on Knowledge and Data Engineering and Journal of Materials Informatics, and program committee members in various first-tier conferences (SIGMOD, VLDB, ICDE, SIGIR, SIGKDD, etc.).