On the stability of spectral graph filters and beyond: A topological perspective


    Department of Systems Engineering and Engineering Management

                       The Chinese University of Hong Kong


Date: Thursday, August 17, 2023, 2:30pm to 3:30pm HKT

Venue: ERB 513, The Chinese University of Hong Kong

Title: On the stability of spectral graph filters and beyond: A topological perspective

Speaker: Dr. Xiaowen Dong, Department of Engineering Science, University of Oxford, UK



Data collected in network domains, hence supported by an irregular graph rather than a regular grid-like structure, are becoming pervasive. Typical examples include gene expression data associated with a protein-protein interaction graph, or behaviours of a group of individuals in a social network. Graph-based signal processing and machine learning are recent techniques that have been developed to handle such graph-structured data and have seen applications in such diverse fields as drug discovery, fake news detection, and traffic prediction. However, a theoretical understanding of the robustness of these models against perturbation to the input graph domain has been lacking. In this talk, I will present our results on the stability bounds of spectral graph filters as well as other recent work on the robustness of graph machine learning models. A commonality of these studies is that they all share a topological perspective, that is, linking robustness to topological properties of the graph domain and perturbation. This contributes to a better understanding of robustness hence the deployment of graph-based models in real-world scenarios.



Xiaowen Dong is an associate professor in the Department of Engineering Science at the University of Oxford, where he is a member of both the Machine Learning Research Group and the Oxford-Man Institute. Prior to joining Oxford, he was a postdoctoral associate in the MIT Media Lab, where he remains as a research affiliate, and received his PhD degree from the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. His main research interests concern signal processing and machine learning techniques for analysing network data, and their applications in studying questions across social and economic sciences.


Everyone is welcome to attend the talk!

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Thursday, August 17, 2023 - 14:30 to 15:30