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
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| 2022-01-23 | |
| Source Publication | WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
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| ISSN | 1386-145X |
| Pages | 20 |
| Abstract | 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. |
| Keyword | Data mining Graph neural networks Social analysis Representation learning |
| DOI | 10.1007/s11280-021-00987-z |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | Youth Innovation Promotion Association CAS[2018192] ; National Natural Science Foundation of China[61902394] |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Information Systems ; Computer Science, Software Engineering |
| WOS ID | WOS:000745550000001 |
| Publisher | SPRINGER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/59900 |
| Collection | 应用数学研究所 |
| Corresponding Author | Shang, Yanmin |
| Affiliation | 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 |
| Recommended Citation 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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