Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining
文献类型：期刊论文
| 作者 | Zhu, Yanqiao5 ; Xu, Yichen4; Yu, Feng3; Liu, Qiang1,2; Wu, Shu1,2
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| 刊名 | ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
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| 出版日期 | 2023-10-01 |
| 卷号 | 14期号:5页码:21 |
| ISSN号 | 2157-6904 |
| 关键词 | Cluster-aware self-training and refining unsupervised learning graph representation learning |
| DOI | 10.1145/3608480 |
| 通讯作者 | Wu, Shu(shu.wu@nlpr.ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | NEURAL-NETWORKS |
| 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] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | ASSOC COMPUTING MACHINERY |
| WOS记录号 | WOS:001087277500006 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54382]  |
| 专题 | 多模态人工智能系统全国重点实验室 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Wu, Shu |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Beijing, Peoples R China 3.DP Technol, 2 Haidian East 3rd St, Beijing 100080, Peoples R China 4.Ecole Polytech Fed Lausanne, CH-1015 Lausanne, Switzerland 5.Univ Calif Los Angeles, 3551 Boelter Hall,580 Portola Plaza, Los Angeles, CA 90095 USA |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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