The Penn State Center for Socially Responsible Artificial Intelligence Distinguished Lecture Series highlights world-renowned scholars of repute who have made fundamental contributions to the advancement of socially responsible artificial intelligence. The series aims to provoke attendees and participants to have thoughtful conversations and to facilitate discussion among students, faculty, and industry affiliates of the Center.
“Network Heterogeneity on Graph Neural Networks”
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.
About the Speaker:
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 138,000 times with an H-index of 172. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data (2011-2017) and IEEE Transactions on Knowledge and Data Engineering (2001-2004).