Dongqi Fu

Bio: Dongqi Fu is a final-year Ph.D. Candidate majoring in Computer Science from University of Illinois at Urbana-Champaign. He is interested on developing data mining and machine learning algorithms on graph data (i.e., non-IID, relational, non-grid, non-Euclidean data). Moreover, the real-world graph data can be (1) related to the temporal information (i.e., time-evolving topological structures, time-evolving node/graph features/labels, etc.) and (2) imperfect (i.e., missing features, scarce labels, hard-to-interpret, redundant, privacy-leaking, robustness-lacking, etc.). Hence, his research focuses on investigating (1) Natural Dynamics (e.g., leveraging spatial-temporal properties of graphs) and (2) Artificial Dynamics (e.g., augmenting and pruning graph components) in Graph Mining, Graph Representations, and Graph Neural Networks to achieve task performance upgrades in accuracy, efficiency, explanation, privacy, fairness, etc., and he is also keen on Graph Data Management and Graph Theory. He used to be a research scientist intern at IBM T.J. Watson Research Center and Meta AI for graph research and applications.

Talk Title: Towards Powerful Graph Learning via Natural and Artificial Dynamics

Abstract: In the era of big data, the relationship between entities has become much more complex than ever before. As a kind of relational data structure, graphs (or networks) attract much research attention for dealing with this
unprecedented phenomenon.

In the long run, graph learning methods face two general challenges when adapting to the complexities of the real world. Firstly, the graph structure and features may change over time (i.e., time-evolving topological structures, time-evolving node/graph features/labels, etc.). The resulting problems include but are not limited to ignoring entity temporal correlation, overlooking causality discovery, computation inefficiency, non-
generalization, etc. Secondly, the initial topological structure and node or graph features may be imperfect (e.g., having construction errors, sampling noises, missing features, scarce labels, hard-to-interpret, redundant, privacy-leaking, robustness-lacking, etc.). The corresponding problems include but are not limited to non- robustness, indiscriminative representations, non-generalization, etc.

Hence, this research talk will concentrate on investigating how to study (1) Natural Dynamics (e.g., leveraging spatial-temporal properties of graphs) and (2) Artificial Dynamics (e.g., augmenting and pruning graph components) for Graph Mining, Graph Representations, and Graph Neural Networks to achieve task performance upgrades in accuracy, efficiency, trustworthiness, etc.