Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

doi:10.1145/3608480

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能感知与计算研究中心

	Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining
	Zhu, Yanqiao 5; Xu, Yichen 4; Yu, Feng 3; Liu, Qiang 1,2; Wu, Shu 1,2
刊名	ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
	2023-10-01
卷号	14 期号:5 页码:21
关键词	Cluster-aware self-training and refining unsupervised learning graph representation learning
ISSN号	2157-6904
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.
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关键词	NEURAL-NETWORKS
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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54382]
专题	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.