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Graph convolutional network with tree-guided anisotropic message passing

文献类型：期刊论文

作者Wang, Ruixiang1,2; Wang, Yuhu1,2; Zhang, Chunxia3; Xiang, Shiming1,2; Pan, Chunhong2
刊名NEURAL NETWORKS
出版日期2023-08-01
卷号165页码:909-924
关键词Deep learning Graph convolutional networks Graph structure learning Anisotropic message passing
ISSN号0893-6080
DOI10.1016/j.neunet.2023.06.034
通讯作者Zhang, Chunxia(cxzhang@bit.edu.cn)
英文摘要Graph Convolutional Networks (GCNs) with naive message passing mechanisms have limited performance due to the isotropic aggregation strategy. To remedy this drawback, some recent works focus on how to design anisotropic aggregation strategies with tricks on feature mapping or structure mining. However, these models still suffer from the low ability of expressiveness and long-range modeling for the needs of high performance in practice. To this end, this paper proposes a tree-guided anisotropic GCN, which applies an anisotropic aggregation strategy with competitive expressiveness and a large receptive field. Specifically, the anisotropic aggregation is decoupled into two stages. The first stage is to establish the path of the message passing on a tree-like hypergraph consisting of substructures. The second one is to aggregate the messages with constrained intensities by employing an effective gating mechanism. In addition, a novel anisotropic readout mechanism is constructed to generate representative and discriminative graph-level features for downstream tasks. Our model outperforms baseline methods and recent works on several synthetic benchmarks and datasets from different real world tasks. In addition, extensive ablation studies and theoretical analyses indicate the effectiveness of our proposed method.& COPY; 2023 Elsevier Ltd. All rights reserved.
资助项目National Key Research and Development Program of China[2020AAA0104 903] ; National Natural Science Foundation of China[62072039] ; National Natural Science Foundation of China[62076242]
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
WOS记录号WOS:001047915500001
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/54128]  
专题多模态人工智能系统全国重点实验室
通讯作者Zhang, Chunxia
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Ruixiang,Wang, Yuhu,Zhang, Chunxia,et al. Graph convolutional network with tree-guided anisotropic message passing[J]. NEURAL NETWORKS,2023,165:909-924.
APA Wang, Ruixiang,Wang, Yuhu,Zhang, Chunxia,Xiang, Shiming,&Pan, Chunhong.(2023).Graph convolutional network with tree-guided anisotropic message passing.NEURAL NETWORKS,165,909-924.
MLA Wang, Ruixiang,et al."Graph convolutional network with tree-guided anisotropic message passing".NEURAL NETWORKS 165(2023):909-924.

入库方式： OAI收割

来源：自动化研究所

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