CNIM-GCN: Consensus Neighbor Interaction-based Multi-channel Graph Convolutional Networks

doi:10.1016/j.eswa.2023.120178

	CNIM-GCN: Consensus Neighbor Interaction-based Multi-channel Graph Convolutional Networks
	Zhu, Xiaofei 1; Li, Chenghong 1; Guo, Jiafeng 2; Dietze, Stefan 3,4
	2023-09-15
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
卷号	226 页码:11
摘要	Node classification plays a critical role in numerous network applications, and has attracted increasing attention in recent years. Existing state-of-the-art studies aim at maintaining common information between the topology graph and the feature graph in an implicit way, i.e., adopting a common convolution with parameter sharing strategy to preserve common information between the two graphs. Despite their effectiveness, these studies are still far from satisfactory due to the complex correlation information between the two spaces. To address this issue, we present a novel method named Consensus Neighbor Interaction-based Multi-channel Graph Convolutional Networks (CNIM-GCN). CNIM-GCN preserves the common information between the feature space and topology space in an explicit way by introducing a consensus graph for information propagation. A multi-channel graph convolutional networks is developed for effectively fusing information from different graphs. In addition, we further incorporate two types of consistency constraints, i.e., structural consistency constraint and reconstruction consistency constraint, to maintain the consistency between different channels. The former is leveraged to keep the consistency between different spaces at the structural relationship level, while the latter is used to preserve a consistency between the final node representation and the original node feature representation. We carry out extensive experiments on five real-world datasets, including ACM, BlogCatalog, CiteSeer, Flickr and UAI2010. Experimental results show that our proposed approach CNIM-GCN is superior to the state-of-the-art baselines.
关键词	Network representation learning Deep learning Graph convolutional networks Node classification
DOI	10.1016/j.eswa.2023.120178
收录类别	SCI
语种	英语
资助项目	National Natural Science Founda-tion of China[62141201] ; Major Project of Science and Technology Research Program of Chongqing Education Commis-sion of China[KJZD-M202201102] ; Natural Sci-ence Foundation of Chongqing, China[CSTB2022NSCQ-MSX1672] ; Federal Ministry of Education and Research, Germany[01IS21086]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:000988858400001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21430
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhu, Xiaofei
作者单位	1.Chongqing Univ Technol, Coll Comp Sci & Engn, Chongqing 400054, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Leibniz Inst Social Sci, Knowledge Technol Social Sci, D-50667 Cologne, Germany 4.Heinrich Heine Univ Dusseldorf, Inst Comp Sci, D-40225 Dusseldorf, Germany
推荐引用方式 GB/T 7714	Zhu, Xiaofei,Li, Chenghong,Guo, Jiafeng,et al. CNIM-GCN: Consensus Neighbor Interaction-based Multi-channel Graph Convolutional Networks[J]. EXPERT SYSTEMS WITH APPLICATIONS,2023,226:11.
APA	Zhu, Xiaofei,Li, Chenghong,Guo, Jiafeng,&Dietze, Stefan.(2023).CNIM-GCN: Consensus Neighbor Interaction-based Multi-channel Graph Convolutional Networks.EXPERT SYSTEMS WITH APPLICATIONS,226,11.
MLA	Zhu, Xiaofei,et al."CNIM-GCN: Consensus Neighbor Interaction-based Multi-channel Graph Convolutional Networks".EXPERT SYSTEMS WITH APPLICATIONS 226(2023):11.