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HMSG: Heterogeneous graph neural network based on Metapath SubGraph learning

文献类型：期刊论文

作者Guan, Mengya3,4; Cai, Xinjun3,4; Shang, Jiaxing3,4; Hao, Fei5; Liu, Dajiang3,4; Jiao, Xianlong3,4; Ni, Wancheng1,2
刊名KNOWLEDGE-BASED SYSTEMS
出版日期2023-11-04
卷号279页码:11
ISSN号0950-7051
关键词Graph neural network Heterogeneous graph Metapath Subgraph Information network
DOI10.1016/j.knosys.2023.110930
通讯作者Shang, Jiaxing(shangjx@cqu.edu.cn)
英文摘要Heterogeneous graph neural network (HGNN) models, capable of learning low-dimensional dense vectors from heterogeneous graphs for downstream graph-mining tasks, have attracted increasing attention in recent years. For these models, metapath-based methods have been widely adopted. However, most existing metapath-based HGNN models either discard intermediate nodes within a metapath, resulting in information loss, or indiscriminately aggregate information along a metapath containing different types of nodes, resulting in unavoidable learning bias. To overcome these limitations, a new HGNN model named HMSG, is proposed in this paper to comprehensively capture structural, semantic and attribute information from both homogeneous and heterogeneous neighbors more purposefully. To achieve this, a type-specific linear transformation is first applied to transfer the node attributes to different types of nodes with the same latent factor space. In the new model, the heterogeneous graph is decomposed into multiple metapath-based homogeneous and heterogeneous subgraphs where each subgraph associates specific semantic and structural information; this is different from existing models, which mainly rely on symmetric metapaths. Subsequently, tailored attention-based message aggregation methods are independently applied to each subgraph such that information learning can be more targeted. Finally, information from different subgraphs is fused through graph-level attention to obtain a complete representation. The learned representations are evaluated by several graph-mining tasks. Results indicate that the HMSG attains the best performance in all evaluation metrics than state-of-the-art baselines. Further ablation experiments demonstrate the effectiveness of the modules designed for the HMSG. (c) 2023 Elsevier B.V. All rights reserved.
WOS关键词MAXIMIZATION
资助项目National Key R&D Program of China[2022YFC3006400] ; National Key R&D Program of China[2022YFC3006402] ; National Natural Science Foundation of China[61966008] ; National Natural Science Foundation of China[U2033213] ; National Natural Science Foundation of China[62072064] ; Natural Science Foundation of Chongqing, China[CSTB2022NSCQ-MSX1017] ; Open Fund of Key Laboratory of Dependable Service Computing in Cyber Physical Society, China[CPSDSC202207] ; Open Fund of the Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China[CASIA-KFKT-10] ; Fundation, China[2020-JCJQ-ZD- 087-00]
WOS研究方向Computer Science
语种英语
出版者ELSEVIER
WOS记录号WOS:001076369600001
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Chongqing, China ; Open Fund of Key Laboratory of Dependable Service Computing in Cyber Physical Society, China ; Open Fund of the Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China ; Fundation, China
源URL[http://ir.ia.ac.cn/handle/173211/53092]  
专题复杂系统认知与决策实验室
通讯作者Shang, Jiaxing
作者单位1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Chongqing Univ, Coll Comp Sci, Chongqing, Peoples R China
4.Chongqing Univ, Key Lab Dependable Serv Comp Cyber Phys Soc, Minist Educ, Chongqing, Peoples R China
5.Shaanxi Normal Univ, Sch Comp Sci, Xian, Peoples R China
推荐引用方式
GB/T 7714		 Guan, Mengya,Cai, Xinjun,Shang, Jiaxing,et al. HMSG: Heterogeneous graph neural network based on Metapath SubGraph learning[J]. KNOWLEDGE-BASED SYSTEMS,2023,279:11.
APA Guan, Mengya.,Cai, Xinjun.,Shang, Jiaxing.,Hao, Fei.,Liu, Dajiang.,...&Ni, Wancheng.(2023).HMSG: Heterogeneous graph neural network based on Metapath SubGraph learning.KNOWLEDGE-BASED SYSTEMS,279,11.
MLA Guan, Mengya,et al."HMSG: Heterogeneous graph neural network based on Metapath SubGraph learning".KNOWLEDGE-BASED SYSTEMS 279(2023):11.

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来源：自动化研究所

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