Graph structures are prevalent across a variety of fields, including social networks, e-commerce, transportation, and biological systems. Within these graphs, numerous analytical and mining tasks can be identified, often aligning with link prediction, node classification, or graph classification. Graph Neural Networks (GNNs) have achieved significant success in these applications, primarily due to their capability to learn powerful graph representations. Nevertheless, the efficacy of GNNs often depends on the availability of labeled data, without which their performance may be compromised. This talk seeks to delve into learning paradigms that diverge from traditional supervised learning, in the context of few-shot learning on graphs. In particular, drawing inspiration from recent progress in language modeling, we pose an intriguing question: Can prompt-based learning be effectively adapted for graph data? Our talk will begin with a comprehensive overview of few-shot learning methodologies on graphs, followed by highlighting some of our representative works in this area.
Speaker Bio
Dr. Yuan Fang is a tenure-track Assistant Professor at the School of Computing and Information Systems at Singapore Management University (SMU). He was previously a data scientist at DBS Bank and a research scientist at A*STAR. His research interests revolve around graph learning and its applications in recommender systems, social network analysis, and science for AI. Dr. Fang has published over 90 papers in leading conferences and journals, and his work has been featured as “Most Influential Paper in WWW’23” (PaperDigest, Sep 2024), as well as “Best Papers of VLDB13” (VLDB Journal Special Issue). He is a Young Associate Editor of Frontiers of Computer Science (Springer & Higher Education Press), and serves various international conferences as organization committee member and area chair.
More Details
- When: Wed. 17 April 2025, at 1-2pm (Brisbane time)
- Speaker: Prof Yuan Fang (SMU)
- Host: Dr Yujun Cai
- Zoom: https://uqz.zoom.us/j/84511228842
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