Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (3): 180-187.doi: 10.11896/jsjkx.231200138

• Database & Big Data & Data Science • Previous Articles     Next Articles

Heterogeneous Graph Attention Network Based on Data Augmentation

YANG Yingxiu1, CHEN Hongmei1,2, ZHOU Lihua1,2 , XIAO Qing1   

  1. 1 School of Information Science and Engineering,Yunnan University,Kunming 650500,China
    2 Yunnan Key Laboratory of Intelligent Systems and Computing,Yunnan University,Kunming 650500,China
  • Received:2023-12-19 Revised:2024-02-02 Online:2025-03-15 Published:2025-03-07
  • About author:YANG Yingxiu,born in 1998,postgra-duate,is a member of CCF(No.R8725G).His main research interests include heterogeneous graph embedding and graph neural networks.
    CHEN Hongmei,born in 1976,Ph.D,associate professor,is a member of CCF(No.49450M).Her main research interests include spatial data mining and location-based social network analysis.
  • Supported by:
    National Natural Science Foundation of China(62266050,62276227),Program for Young and Middle-aged Academic and Technical Reserve Leaders of Yunnan Province(202205AC160033),Yunnan Fundamental Research Projects(202201AS070015) and Program of Yunnan Key Laboratory of Intelligent Systems and Computing(202405AV340009).

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Abstract: Heterogeneous graph is a graph composed of different types of nodes and edges,which can model various types of objects and their relationships in the real world.Heterogeneous graph embedding aims to learn the embedding vectors of nodes by capturing rich attribute,structural and semantic information in the graph,which can be used in tasks such as node classification and link prediction,further achieving applications such as user recognition and product recommendation.The existing embedding methods exploit meta-paths to capture high-order structural and semantic information between nodes.However,the existing methods ignore the differences between different types of nodes in meta-path instances or different types of neighbor nodes in the graph,resulting in information loss,which in turn affects the quality of node embedding.In view of the above issues,this paper proposes heterogeneous graph attention network based on data augmentation(HANDA) to better learn the embedding vectors of nodes.Firstly,edge augmentation method based on meta-path neighbors is proposed.The method obtains neighbors of nodes based on meta-paths,and generates semantic edges between nodes and meta-path neighbors.These edges not only contain high-order structural and semantic information between nodes,but also alleviate the sparsity issue of the graph.Secondly,a node embedding method incorporating node type attention is presented.The method adopts the multi-head attention incorporating node types to obtain the importance of neighbors formed by both edges and semantic edges.Further,the method simultaneously captures attribute,high-order structural and semantic information by message passing and two kinds of neighbors,resulting in improving the embedding vectors of nodes.Experimental results on real datasets show that the proposed HANDA outperforms the baselines in both node classification and link prediction.

Key words: Heterogeneous graph, Embedding, Meta-path, Data augmentation, Graph neural network

CLC Number: 

  • TP391
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