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• Learning graphs from data; A signal processing perspective

Learning Graphs from Data; A Signal Processing Perspective

Abstract

The construction of a meaningful graph topology plays a crucial role in the success of many graph-based representations and algorithms for handling structured data. When a good choice of the graph is not readily available, however, it is often desirable to infer the graph topology from the observed data. In this talk, I will first survey classical solutions to the problem of graph learning from a machine learning viewpoint. I will then discuss a series of recent works from the fast-growing field of graph signal processing (GSP) and show how signal processing tools and concepts can be utilized to provide novel solutions to this important problem. Finally, I will end with some of the open questions and challenges that are central to the design of future signal processing and machine learning algorithms for graph learning.

Bio

Xiaowen Dong is a Departmental Lecturer (roughly Assistant Professor) in the Department of Engineering Science and a Faculty Member of the Oxford-Man Institute, University of Oxford. He is primarily interested in developing novel techniques that lie at the intersection of machine learning, signal processing, and game theory in the context of networks, and applying them to study questions across social and economic sciences, with a particular focus on understanding human behaviour, decision making and societal changes.