Learning Graphs From Data: A Signal Representation Perspective

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.


Published in:
IEEE Signal Processing Magazine, 36, 3, 44-63
Year:
May 01 2019
Other identifiers:
Laboratories:




 Record created 2019-04-29, last modified 2019-05-22


Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)