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.

翻译：异质图神经网络旨在从多关系网络中挖掘具备判别力的节点嵌入与关系。异质图学习的一大挑战在于可学习元路径的设计，这直接影响所学习嵌入的质量。为此，本文提出了一种属性化多阶图卷积网络（AMOGCN），该网络通过对多阶邻接矩阵的自适应聚合，自动学习包含多跳邻居的元路径。所提模型首先基于人工设计的节点连接构建不同阶次的邻接矩阵，随后通过多阶邻接矩阵的自动融合生成一个完整的综合邻接矩阵。该过程受节点语义信息监督，该语义信息来自通过属性评估的节点同质性。最后，我们利用所学习到的多阶邻接矩阵，结合单层简化图卷积网络——这等价于多层图神经网络中的跨跳节点信息传播。大量实验表明，AMOGCN在半监督分类任务上取得了优于现有最优方法的性能。