Graph Representation Learning for Network Generation, Optimization, and Verbalization

Date: Oct 4, 2:05pm – 3:15pm

Abstract: Graphs are ubiquitous data structure that denotes entities and their relations, such as social networks, citation graphs, and neural networks. The topology of graphs is discrete data which prevents it from enjoying numerous mathematical and statistical tools that requires structured data. Graph representation learning aims to map graphs to their vector representations without substantial information loss, hence pave a new pathway for solving graph problems without discrete algorithms. In this talk, I will first introduce our recent works on graph representation learning that can preserve graphs’ geometric information and properties. Then, I will exemplify several interesting research areas where their problem-solving benefits from our leveraging of graph representations. The first area is to solve graph optimization problems, such as influence maximization, source localization, etc., using continuous optimization over graph representations. The second area is to capture and predict deep learning models’ dynamics over data distribution drifts, where the graph representation of neural networks is learned to reflect their functional space. The third area is to investigate the correlation and difference of the two views of graphs in mathematical language and natural language, where the graph representation acts as their bridge, with the help of large language models.

Biographical Sketch: Dr. Liang Zhao is an associate professor at the Department of Compute Science at Emory University. Before that, he was an assistant professor in the Department of Information Science and Technology and the Department of Computer Science at George Mason University. He obtained his Ph.D. degree as Outstanding PhD student in 2016 from Computer Science Department at Virginia Tech in the United States. His research interests include data mining and machine learning, with special interests in spatiotemporal and network data
mining, deep learning on graphs, language and multimodal foundation models, distributed optimization, and interpretable machine learning. He has published over a hundred papers in top-tier conferences and journals such as KDD, TKDE, ICDM, ICLR, NeurIPS, Proceedings of the IEEE, TKDD, CSUR, IJCAI, AAAI, and WWW. He won NSF CAREER Award and Middle-Career Award by IEEE Computer Society on Smart Computing. He also obtained many prestigious awards from industry such as Meta Research Award, Amazon Research Award, Cisco Faculty Research Award, and Jeffress Trust Award. He was recognized as one of the “Top 20 Rising Star in Data Mining” by Microsoft Search in 2016. He has won several best paper awards and shortlists, such as the Best Paper Award of ICDM 2022, Best Poster Runner-up Award of ACM SIGSPATIAL 2022, Best Paper Award Shortlist in WWW 2021, Best Paper Candidate in ACM SIGSPATIAL 2022, the Best Paper Award of ICDM 2019, and Best Paper Candidate in ICDM 2021. He is recognized as a “Computing Innovative Fellow Mentor” in 2021 by Computing Research Association. He is a senior member of IEEE.

Location and Zoom link: 307 Love, or https://fsu.zoom.us/j/4842912469