Learning graphs from data: a signal representation perspective

Journal article

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis, and visualization of structured data. When a natural choice of the graph is not readily available from the data sets, it is thus desirable to infer or learn a graph topology from the data. In this article, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal processing (GSP) perspective. We further emphasize the conceptual similarities and differences between classical and GSP-based graph-inference methods and highlight the potential advantage of the latter in a number of theoretical and practical scenarios. We conclude with several open issues and challenges that are keys to the design of future signal processing and machine-learning algorithms for learning graphs from data.

Authors: Dong, X; Thanou, D; Rabbat, M; Frossard, P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Journal: IEEE Signal Processing Magazine
• Volume: 36
• Issue: 3
• Pages: 44-63
• Publication date: 2019-04-26
• Acceptance date: 2018-11-16
• DOI: 10.1109/MSP.2018.2887284
• EISSN: 1558-0792
• ISSN: 1053-5888