Knowledge Commons of Institute of Automation,CAS
Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining | |
Zhu, Yanqiao1![]() ![]() ![]() ![]() | |
发表期刊 | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
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ISSN | 2157-6904 |
2023-10-01 | |
卷号 | 14期号:5页码:21 |
摘要 | Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled nodes, which may not be accessible in real-world applications. To this end, we present a novel unsupervised graph neural network model with Cluster-aware Self-training and Refining (CLEAR). Specifically, in the proposed CLEAR model, we perform clustering on the node embeddings and update the model parameters by predicting the cluster assignments. To avoid degenerate solutions of clustering, we formulate the graph clustering problem as an optimal transport problem and leverage a balanced clustering strategy. Moreover, we observe that graphs often contain inter-class edges, which mislead the GNN model to aggregate noisy information from neighborhood nodes. Therefore, we propose to refine the graph topology by strengthening intra-class edges and reducing node connections between different classes based on cluster labels, which better preserves cluster structures in the embedding space. We conduct comprehensive experiments on two benchmark tasks using real-world datasets. The results demonstrate the superior performance of the proposed model over baseline methods. Notably, our model gains over 7% improvements in terms of accuracy on node clustering over state-of-the-arts. |
关键词 | Cluster-aware self-training and refining unsupervised learning graph representation learning |
DOI | 10.1145/3608480 |
关键词[WOS] | NEURAL-NETWORKS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[62141608] ; National Natural Science Foundation of China[U19B2038] ; National Natural Science Foundation of China[62206291] |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Information Systems |
WOS记录号 | WOS:001087277500006 |
出版者 | ASSOC COMPUTING MACHINERY |
七大方向——子方向分类 | 模式识别基础 |
国重实验室规划方向分类 | 智能计算与学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/54383 |
专题 | 多模态人工智能系统全国重点实验室 模式识别实验室 |
通讯作者 | Wu, Shu |
作者单位 | 1.Univ Calif Los Angeles, 3551 Boelter Hall,580 Portola Plaza, Los Angeles, CA 90095 USA 2.Ecole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland 3.DP Technol, 2 Haidian East 3rd St, Beijing 100080, Peoples R China 4.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhu, Yanqiao,Xu, Yichen,Yu, Feng,et al. Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2023,14(5):21. |
APA | Zhu, Yanqiao,Xu, Yichen,Yu, Feng,Liu, Qiang,&Wu, Shu.(2023).Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,14(5),21. |
MLA | Zhu, Yanqiao,et al."Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 14.5(2023):21. |
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