Community detection with graph neural networks



We study data-driven methods for community detection in graphs. This estimation problem is typically formulated in terms of the spectrum of certain operators, as well as via posterior inference under certain probabilistic graphical models. Focusing on random graph families such as the Stochastic Block Model, recent research has unified these two approaches, and identified both statistical and computational signal-to-noise detection thresholds.


Joan Bruna
X Li