MMAN: Metapath Based Multi-Level Graph Attention Networks for Heterogeneous Network Embedding (Student Abstract)

Current Heterogeneous Network Embedding (HNE) models can be roughly divided into two types, i. e. , relation-aware and metapath-aware models. However, they either fail to represent the non-pairwise relations in heterogeneous graph, or only capable of capturing local information around target node. In this paper, we propose a metapath based multilevel graph attention networks (MMAN) to jointly learn node embeddings on two substructures, i. e. , metapath based graphs and hypergraphs extracted from original heterogeneous graph. Extensive experiments on three benchmark datasets for node classification and node clustering demonstrate the superiority of MMAN over the state-of-the-art works.

Authors

• Jie Liu
• Lingyun Song Northwest Polytechnical University
• Li Gao Baidu Inc.
• Xuequn Shang

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