Heterogeneous Graph Convolutional Networks for Temporal Community Detection

The Graph Convolutional Networks (GCN) has demonstrated superior performance in representing graph data, especially homogeneous graphs. However, the real-world graph data is usually heterogeneous and evolves with time, e.g., Facebook and DBLP, which has seldom been studied. To cope with this issue, we propose a novel approach named temporal heterogeneous graph convolutional network (THGCN). THGCN first embeds both spatial information and node attribute information together. Then, it captures short-term evolutionary patterns from the aggregations of embedded graph signals through compression network. Meanwhile, the long-term evolutionary patterns of heterogeneous graph data are also modeled via a TCN temporal convolutional network. To the best of our knowledge, this is the first attempt to model temporal heterogeneous graph data with a focus on community discovery task.