A graph convolutional fusion model for community detection in multiplex networks

Community detection is to partition a network into several components, each of which contains densely connected nodes with some structural similarities. Recently, multiplex networks, each layer consisting of a same node set but with a different topology by a unique edge type, have been proposed to model real-world multi-relational networks. Although some heuristic algorithms have been extended into multiplex networks, little work on neural models have been done so far. In this paper, we propose a graph convolutional fusion model (GCFM) for community detection in multiplex networks, which takes account of both intra-layer structural and inter-layer relational information for learning node representation in an interwoven fashion. In particular, we first develop a graph convolutional auto-encoder for each network layer to encode neighbor-aware intra-layer structural features under different convolution scales. We next design a multiscale fusion network to learn a holistic version of nodes' representations by fusing nodes' encodings at different layers and different scales. Finally, a self-training mechanism is used to train our model and output community divisions. Experiment results on both synthetic and real-world datasets indicate that the proposed GCFM outperforms the state-of-the-art techniques in terms of better detection performances.