Learning Graphs From Data
Learning Graphs From Data
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DOI：
10.1109/msp.2018.2887284
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发表时间：
2019-05-01
影响因子：
14.9
通讯作者：
Frossard, Pascal
中科院分区：
文献类型：
--
作者：
Dong, Xiaowen;Thanou, Dorina;Frossard, Pascal
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.
