K-Core based Temporal Graph Convolutional Network for Dynamic Graphs.

Graph representation learning is a fundamental task of various applications, aiming to learn low-dimensional embeddings for nodes which can preserve graph topology information. However, many existing methods focus on static graphs while ignoring graph evolving patterns. Inspired by the success of graph convolutional networks(GCNs) in static graph embedding, we propose a novel k-core based temporal graph convolutional network, namely CTGCN, to learn node representations for dynamic graphs. In contrast to previous dynamic graph embedding methods, CTGCN can preserve both local connective proximity and global structural similarity in a unified framework while simultaneously capturing graph dynamics. In the proposed framework, the traditional graph convolution operation is generalized into two parts: feature transformation and feature aggregation, which gives CTGCN more flexibility and enables CTGCN to learn connective and structural information under the same framework. Experimental results on 7 real-world graphs demonstrate CTGCN outperforms existing state-of-the-art graph embedding methods in several tasks, such as link prediction and structural role classification. The source code of this work can be obtained from https://github.com/jhljx/CTGCN.