Graph Machine Learning: Theory, Technology and Applications

Talk By Chang-Dong WANG

Feb 29, 2024 Thursday


As an important branch of machine learning, graph machine learning is a discipline that studies how to use topological structure data for machine learning. Different from the traditional machine learning, which mainly targets vector and matrix data, the data processed by graph machine learning is topological data with explicit relations. Its main advantage is that it can effectively deal with unstructured data and complex relationships, and mine potential correlation patterns and laws. In this talk, we will introduce the theory, technology and applications of graph machine learning from the following three aspects: 1) Graph machine learning based on information mapping; 2) Graph clustering based on multi-level semantic information fusion; 3) Recommendation algorithms and applications based on heterogeneous graph. Finally, as a frontier, we explore the pre-training-based cross-graph and cross-task graph machine learning.


Feb 29, 2024 Thursday



Rm W1-101, GZ Campus

Online Zoom

Join Zoom at OR 423 685 2791

Speaker Bio:

Chang-Dong WANG

Associate Professor, School of Computer Science and Engineering, Sun Yat-sen University

Chang-Dong Wang received the Ph.D. degree in computer science in 2013 from Sun Yat-sen University, Guangzhou, China. He was a visiting student at University of Illinois at Chicago from Jan. 2012 to Nov. 2012. He joined Sun Yat-sen University in 2013, where he is currently an associate professor with School of Computer Science and Engineering. His current research interests include machine learning and data mining. He has published over 80 scientific papers in international journals and conferences such as IEEE TPAMI, IEEE TKDE, IEEE TCYB, IEEE TNNLS, ACM TKDD, ACM TIST, IEEE TSMC-Systems, IEEE TII, IEEE TSMC-C, KDD, AAAI, IJCAI, CVPR, ICDM, CIKM and SDM. His ICDM 2010 paper won the Honorable Mention for Best Research Paper Awards. He won 2012 Microsoft Research Fellowship Nomination Award. He was awarded 2015 Chinese Association for Artificial Intelligence (CAAI) Outstanding Dissertation. He is an Associate Editor in Journal of Artificial Intelligence Research (JAIR) and Neural Networks. He is a Senior Member of IEEE.