Graph Learning and its Applications
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Graph, as a very expressive model, has been widely used to model real-world entities and their relationships in application-specific networks, e.g., social networks, road networks, biological networks, communication networks, etc. The ubiquity of such networks, the ever-increasing size, the dynamic nature, and the rich semantics have brought us a lot of research opportunities as well as new challenges. We need in-depth, efficient and scalable mining and analysis tools to discover the hidden knowledge from these massive and complex networks and further enhance our understanding. In this talk, I shall discuss the recent development of machine learning on graph structure data and its applications. I shall pay special attention to the robustness and interpretability of graph learning.
Jia LI is currently a PhD candidate at The Chinese University of Hong Kong. Jia LI’s current research mainly lies in the areas of machine learning and data mining, with a focus on developing models that analyze graph data. The major research topics and outputs include dynamic/hierarchical graph representation, robustness in graph learning, interpretability in graph learning, scalability in graph learning and health care.