Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs

no code implementations • 13 Apr 2023 • Zhaoliang Chen, Zhihao Wu, Luying Zhong, Claudia Plant, Shiping Wang, Wenzhong Guo

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.

Graph Learning