Community detection in graphs is of central importance in graph mining, machine learning and network science.  Detecting overlapping communities is especially challenging, and remains an open problem .  Motivated by the success of graph-based  deep  learning  in  other  graph-related  tasks,  we  study  the  applicability  of this framework for overlapping community detection . We propose a probabilistic model for overlapping community detection based on the graph neural network architecture .  Despite its simplicity, our model outperforms the existing approaches in the community recovery task by a large margin .  Moreover, due to the inductive formulation, the proposed model is able to perform out-of-sample community detection for nodes that were not present at training time
Detecting overlapping communities in graphs using graph neural networks