KMS Of Academy of mathematics and systems sciences, CAS
API-GNN: attribute preserving oriented interactive graph neural network | |
Zhou, Yuchen1,2; Shang, Yanmin1,2; Cao, Yanan1,2; Li, Qian3; Zhou, Chuan1,4; Xu, Guandong3 | |
2022-01-23 | |
发表期刊 | WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS |
ISSN | 1386-145X |
页码 | 20 |
摘要 | Attributed graph embedding aims to learn node representation based on the graph topology and node attributes. The current mainstream GNN-based methods learn the representation of the target node by aggregating the attributes of its neighbor nodes. These methods still face two challenges: (1) In the neighborhood aggregation procedure, the attributes of each node would be propagated to its neighborhoods which may cause disturbance to the original attributes of the target node and cause over-smoothing in GNN iteration. (2) Because the representation of the target node is derived from the attributes and topology of its neighbors, the attributes and topological information of each neighbor have different effects on the representation of the target node. However, this different contribution has not been considered by the existing GNN-based methods. In this paper, we propose a novel GNN model named API-GNN (Attribute Preserving Oriented Interactive Graph Neural Network). API-GNN can not only reduce the disturbance of neighborhood aggregation to the original attribute of target node, but also explicitly model the different impacts of attribute and topology on node representation. We conduct experiments on six public real-world datasets to validate API-GNN on node classification and link prediction. Experimental results show that our model outperforms several strong baselines over various graph datasets on multiple graph analysis tasks. |
关键词 | Data mining Graph neural networks Social analysis Representation learning |
DOI | 10.1007/s11280-021-00987-z |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Youth Innovation Promotion Association CAS[2018192] ; National Natural Science Foundation of China[61902394] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering |
WOS记录号 | WOS:000745550000001 |
出版者 | SPRINGER |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.amss.ac.cn/handle/2S8OKBNM/59900 |
专题 | 应用数学研究所 |
通讯作者 | Shang, Yanmin |
作者单位 | 1.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China 3.Univ Technol Sydney, Sydney, NSW, Australia 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Zhou, Yuchen,Shang, Yanmin,Cao, Yanan,et al. API-GNN: attribute preserving oriented interactive graph neural network[J]. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,2022:20. |
APA | Zhou, Yuchen,Shang, Yanmin,Cao, Yanan,Li, Qian,Zhou, Chuan,&Xu, Guandong.(2022).API-GNN: attribute preserving oriented interactive graph neural network.WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,20. |
MLA | Zhou, Yuchen,et al."API-GNN: attribute preserving oriented interactive graph neural network".WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS (2022):20. |
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