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.
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 . To address the above issues, we propose an interpretable and efficient Heterogeneous Graph Convolutional Network (ie-HGCN) to learn the representations of objects in HINs. It is designed as a hierarchical aggregation architecture, i.e., object-level aggregation first, followed by type-level aggregation. The novel architecture can automatically extract useful meta-paths for each object from all possible meta-paths (within a length limit ), which brings good model interpretability. It can also reduce the computational cost by avoiding intermediate HIN transformation and neighborhood attention. We provide theoretical analysis about the proposed ie-HGCN in terms of evaluating the usefulness of all possible meta-paths , its connection to the spectral graph convolution on HINs, and its quasi-linear time complexity. Extensive experiments on three real network datasets demonstrate the superiority of ie-HGCN over the state-of-the-art methods.
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