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.
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