Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs
This work addresses the problem of improving node embeddings in heterogeneous graphs for researchers and practitioners in graph neural networks, representing an incremental advancement.
The paper tackles the challenge of designing learnable meta-paths for heterogeneous graph learning by proposing AMOGCN, which automatically studies meta-paths through adaptive aggregation of multi-order adjacency matrices, resulting in superior semi-supervised classification performance compared to state-of-the-art competitors.
Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks.One challenge of heterogeneous graph learning is the design of learnable meta-paths, which significantly influences the quality of learned embeddings.Thus, in this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors from an adaptive aggregation of multi-order adjacency matrices. The proposed model first builds different orders of adjacency matrices from manually designed node connections. After that, an intact multi-order adjacency matrix is attached from the automatic fusion of various orders of adjacency matrices. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we utilize a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN gains superior semi-supervised classification performance compared with state-of-the-art competitors.