Interpretable and Efficient Heterogeneous Graph Convolutional Network

27 May 2020 • Yaming Yang • Ziyu Guan • Jian-Xin Li • Wei Zhao • Jiangtao Cui • Quan Wang

Graph Convolutional Network (GCN) has achieved extraordinary success in learning effective task-specific representations of nodes in graphs. However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore all possible meta-paths and extract the most useful ones for a target object, which hinders both effectiveness and interpretability; (2) they often need to generate intermediate meta-path based dense graphs, which leads to high computational complexity... (read more)

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