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Graph convolutional network with tree-guided anisotropic message passing | |
Wang, Ruixiang1,2; Wang, Yuhu1,2; Zhang, Chunxia3; Xiang, Shiming1,2; Pan, Chunhong1 | |
发表期刊 | NEURAL NETWORKS |
ISSN | 0893-6080 |
2023-08-01 | |
卷号 | 165页码:909-924 |
通讯作者 | Zhang, Chunxia(cxzhang@bit.edu.cn) |
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. |
关键词 | Deep learning Graph convolutional networks Graph structure learning Anisotropic message passing |
DOI | 10.1016/j.neunet.2023.06.034 |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science ; Neurosciences & Neurology |
WOS类目 | Computer Science, Artificial Intelligence ; Neurosciences |
WOS记录号 | WOS:001047915500001 |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/54129 |
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.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
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. |
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