Interpretable and Efficient Heterogeneous Graph Convolutional Network
This addresses efficiency and interpretability issues in heterogeneous information networks for machine learning practitioners, though it is incremental as it builds on existing HGCN methods.
The paper tackled the problem of existing heterogeneous graph convolutional networks (HGCNs) being inefficient and lacking interpretability in exploring meta-paths, proposing an interpretable and efficient HGCN (ie-HGCN) that automatically extracts useful meta-paths and reduces computational cost, achieving superior performance over state-of-the-art methods on three real datasets.
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.