Learning node representation via Motif Coarsening

doi:10.1016/j.knosys.2023.110821

	Learning node representation via Motif Coarsening
	Yan, Rong 1; Bao, Peng 1; Shen, Huawei 2; Li, Xuanya 3
	2023-10-25
发表期刊	KNOWLEDGE-BASED SYSTEMS
ISSN	0950-7051
卷号	278 页码:12
摘要	Motifs, as fundamental units of the graph, play a significant role in modeling complex systems in a variety of domains, including social networks, as well as biology and neuroscience. Motif preservation is a widely studied problem that provides new avenues for structure preservation. This paper is dedicated to exploring the significance of motifs with various patterns and effectively incorporating different motif patterns into node-level graph representation learning. We propose a novel node Representation learning framework via Motif Coarsening (RMC), which effectively incorporates different granularity structural information into node representation learning. RMC consists of two parallel components, the node representation learning aggregator and the motif-based node representation learning aggregator. In the node representation learning process, RMC directly encodes lower-order structures into node representation by a one-layer graph convolution network. For the motif-based node representation learning process, we propose a Motif Coarsening strategy for incorporating motif structure into the graph representation learning process. Furthermore, the MotifRe-Weighting strategy is proposed to bi-ased convert motif representation into motif-based node representation. We verify the effectiveness of RMC by several node-related tasks on a series of widely used real-world datasets. Experimental results demonstrate that our proposed framework delivers superior promising representation performance to existing benchmarks. Ablation experiments proved that RMC has potential as an auxiliary framework, which indicates the excellent quality of Motif Coarsening and MotifRe-Weighting strategies over existing benchmarks from several evaluation metrics, involving mean classification accuracy, Micro-F1, and Macro-F1. & COPY; 2023 Elsevier B.V. All rights reserved.
关键词	Graph representation learning Motif Coarsening MotifRe-Weighting
DOI	10.1016/j.knosys.2023.110821
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U21B2046] ; CCF-Tencent Open Research Fund ; CAAI-Huawei MindSpore Open Fund ; [62272032]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001059151000001
出版者	ELSEVIER
引用统计	被引频次：1[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/2XEOYT63/21390
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Bao, Peng
作者单位	1.Beijing Jiaotong Univ, Sch Software Engn, Beijing 100081, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Data Intelligence Syst Res Ctr, Beijing 100190, Peoples R China 3.Baidu Inc, Beijing 100085, Peoples R China
推荐引用方式 GB/T 7714	Yan, Rong,Bao, Peng,Shen, Huawei,et al. Learning node representation via Motif Coarsening[J]. KNOWLEDGE-BASED SYSTEMS,2023,278:12.
APA	Yan, Rong,Bao, Peng,Shen, Huawei,&Li, Xuanya.(2023).Learning node representation via Motif Coarsening.KNOWLEDGE-BASED SYSTEMS,278,12.
MLA	Yan, Rong,et al."Learning node representation via Motif Coarsening".KNOWLEDGE-BASED SYSTEMS 278(2023):12.