Network Heterogeneity on Graph Neural Networks

When:
Friday, June 18, 2021, 11:00 am - 12:00 pm PDTiCal
Where:
Virtual via Zoom WebinarThis event is open to the public.
Type:
AI Seminar
Speaker:
Philip S. Yu (University of Illinois-Chicago)
Description:

This Talk Will Not Be Recorded

AI Seminar Series

Seminars for the Artificial Intelligence Division at USC Information Sciences Institute

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Abstract:

Graph neural network (GNN), as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. Basically, the current GNNs follow the message-passing framework which receives messages from neighbors and applies neural network to learn node representations. However, previous GNNs mainly focus on homogeneous graph, while in reality, the real-world graphs usually are far from homogeneity. Here we first examine the various types of network heterogeneity, including node and link type heterogeneity,neighborhood heterogeneity, fragment heterogeneity, temporal heterogeneity, and structure heterogeneity. We then discuss the implications and methods to overcome these heterogeneities.

Bio:

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. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. 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 IEEE Intl. Conference on Data Mining (ICDM) in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,300 referred conference and journal papers cited more than 139,000 times with an H-index of 172. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).

Host: Keith Burghardt, POC: Pete Zamar

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