Title: Overlapping Community Detection with Graph Neural Networks. Abstract: 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