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 Graphs provide a natural way of representing complex real-world systems. For understanding the structure and behavior of these systems, community detection methods are an essential tool. Detecting communities allows us to analyze social networks BID9 , to detect fraud BID24 , to discover functional units of the brain BID8 , and to predict functions of proteins BID26 . Over the last decades, this problem has attracted significant attention of the research community and numerous models and algorithms have been proposed BID32 . In particular, it is a well known fact that communities in real graphs are in fact overlapping BID34 , thus, requiring the development of advanced models to capture this complex structure.In this regard, the advent of deep learning methods for graph-structured data opens new possibilities for designing more accurate and more scalable algorithms. Indeed, deep learning on graphs has already shown state-of-the-art results in s for various graph-related tasks such as semi-supervised node classification and link prediction BID2 . Likewise, a few deep learning methods for community detection in graphs have been proposed BID36 BID4 . However, they all have one drawback in common: they only focus on the special case of disjoint (non-overlapping) communities. Handling overlapping communities, is a requirement not yet met by existing deep learning approaches to community detection.In this paper we propose an end-to-end deep probabilistic model for overlapping community detection in graphs. Our core idea lies in predicting the community affiliations using a graph neural network. Despite its simplicity, our model achieves state-of-the art results in community recovery and significantly outperforms the existing approaches. Moreover, our model is able to perform out-of-sample (inductive) community detection for nodes that were not seen at training time. To summarize, our main contributions are:• We propose the Deep Overlapping Community detection (DOC) model -a simple, yet effective deep learning model for overlapping community detection in graphs. DOC is one of few methods is able to perform community detection both transductively and inductively.• We introduce 5 new datasets for overlapping community detection, which can act as a benchmark to stimulate future work in this research area.• We perform a thorough experimental evaluation of our model, and show its superior performance when comparing with established methods for overlapping community detection. In this work we have proposed and studied two deep models for overlapping community detection: DOC-BP, based on the Bernoulli-Poisson link, and DOC-Sigmoid, that relies on the sigmoid link function. The two variants of our model achieve state-of-the-art results and convincingly outperfom existing techniques in transductive and inductive community detection tasks. Using stochastic training, both approaches are highly efficient and scale to large graphs. Among the two proposed models, DOC-BP one average performs better than the DOC-Sigmoid variant. We leave to future work to investigate the properties of communities detected by these two methods.To summarize, the results obtained in our experimental evaluation provide strong evidence that deep learning for graphs deserves more attention as a framework for overlapping community detection.
Detecting overlapping communities in graphs using graph neural networks
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