Dong, XiaowenThanou, DorinaRabbat, MichaelFrossard, Pascal2019-04-292019-04-292019-04-292019-05-0110.1109/MSP.2018.2887284https://infoscience.epfl.ch/handle/20.500.14299/1561541806.00848The 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.ml-tmtext::journal::journal article::research article