Community Detection with Graph Neural Networks

ICLR 2018 Zhengdao ChenXiang LiJoan Bruna

We study data-driven methods for community detection on graphs, an inverse problem that is typically solved in terms of the spectrum of certain operators or via posterior inference under certain probabilistic graphical models. Focusing on random graph families such as the stochastic block model, recent research has unified both approaches and identified both statistical and computational signal-to-noise detection thresholds... (read more)

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