Interpretable and Efficient Heterogeneous Graph Convolutional Network
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Graph Convolutional Network (GCN) has achieved extraordinary success in learning effective high-level representations of nodes in graphs. However, the study regarding Heterogeneous Information Network (HIN) is still limited, because the existing HIN-oriented GCN methods suffer from two deficiencies: (1) they cannot flexibly exploit all possible meta-paths, and some even require the user to specify useful ones; (2) they often need to first transform an HIN into meta-path based graphs by computing commuting matrices, which has a high time complexity, resulting in poor scalability. To address the above issues, we propose interpretable and efficient Heterogeneous Graph Convolutional Network (ie-HGCN) to learn representations of nodes in HINs. It automatically extracts useful meta-paths for each node from all possible meta-paths (within a length limit determined by the model depth), which brings good model interpretability. It directly takes the entire HIN as input and avoids intermediate HIN transformation. The carefully designed hierarchical aggregation architecture avoids computationally inefficient neighborhood attention. Thus, it is much more efficient than previous methods. We formally prove ie-HGCN evaluates the usefulness of all possible meta-paths within a length limit (model depth), show it intrinsically performs spectral graph convolution on HINs, and analyze the time complexity to verify its quasi-linear scalability. Extensive experimental results on three real-world networks demonstrate the superiority of ie-HGCN over state-of-the-art methods.
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