Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MSP.2018.2887284 | IEEE Signal Processing Magazine |
DocType | Volume | Issue |
Journal | abs/1806.00848 | 3 |
ISSN | Citations | PageRank |
1053-5888 | 20 | 0.79 |
References | Authors | |
37 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaowen Dong | 1 | 249 | 22.07 |
| Dorina Thanou | 2 | 147 | 11.83 |
| Michael G. Rabbat | 3 | 1631 | 111.76 |
| Pascal Frossard | 4 | 3015 | 230.41 |