Self-supervised community detection in multiplex networks with graph convolutional autoencoder

Abstract

Community detection in multiplex networks has received considerable attention in recent years. However, existing methods that combine graph embedding and downstream tasks still face two challenges. The first is how to fully explore the correlation among the layers in the multiplex networks, and the second is how to make the learned node representation more applicable to the community detection tasks. Aiming at these challenges, we propose a novel self-supervised multiplex community detection model called MGCAE which is based on graph neural networks. To solve the first challenge, we compute the mutual information maximization loss in the self-supervision module. The mutual information includes global representation and common representation of nodes in different layers, and node representation in each layer. For the second challenge, we combine a Bernoulli-Poisson loss and a modularity maximization loss to jointly optimize the reconstruction of the original adjacency matrix, which is in line with the rigorous theory of modularity. We treat graph convolutional autoencoder (GCAE) as the backbone framework and train it by using the unified loss mentioned above. In addition, the model obtains the community detection results in an end-to-end manner, which makes the model independent of downstream tasks and more stable. Experiments on real-world attributed multiplex network datasets demonstrate the effectiveness of our model.