报告题目：Geometric Deep Graph Learning: Exploring Opportunities in Different Geometric Spaces
主讲人：Dr. Philip S. Yu
报告时间：7月26日（星期五）15:00
主讲人介绍：
Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data” and the Research Contributions Award from ICDM in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,600 referred conference and journal papers cited more than 200,000 times with an H-index of 198. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM TKDD (2011-2017) and IEEE TKDE (2001-2004).
报告内容摘要：
Graph is a ubiquitous non-Euclidean structure, describing the intercorrelated objects in the complex system, ranging from social networks, transportation systems, financial transaction networks to biochemical and molecule structures. Nowadays, graph neural networks are becoming the de facto solution for learning on graphs, generating node or graph embeddings in representation space, such as the traditional Euclidean space. However, a natural and fundamental question that has been rarely explored is: which representation space is more suitable for complex graphs? In fact, the "flat" Euclidean space is suitable for grid structures but is not geometrically aligned with generic graphs with complex structures. Thus, it is interesting to explore deep graph learning in different geometric spaces. This talk will delve into the fascinating facts and properties of various geometric spaces (e.g., hyperbolic space and hyperspherical space), and discuss some preliminary works on tasks such as classification, clustering, contrastive learning, graph structure learning, and continual graph learning. These endeavors pave the way for the next-generation of deep graph learning.
