Temporal gravity model for important node identification in temporal networks

doi:10.1016/j.chaos.2021.110934

CSpace > 应用数学研究所

	Temporal gravity model for important node identification in temporal networks
	Bi, Jialin 1; Jin, Ji 1; Qu, Cunquan 1,2; Zhan, Xiuxiu 3; Wang, Guanghui 1,2; Yan, Guiying4,5
	2021-06-01
发表期刊	CHAOS SOLITONS & FRACTALS
ISSN	0960-0779
卷号	147 页码:17
摘要	Identifying important nodes in networks is essential to analysing their structure and understanding their dynamical processes. In addition, myriad real systems are time-varying and can be represented as temporal networks. Motivated by classic gravity in physics, we propose a temporal gravity model to identify important nodes in temporal networks. In gravity, the attraction between two objects depends on their masses and distance. For the temporal network, we treat basic node properties (e.g., static and temporal properties) as the mass and temporal characteristics (i.e., fastest arrival distance and temporal shortest distance) as the distance. Experimental results on 10 real datasets show that the temporal gravity model outperforms baseline methods in quantifying the structural influence of nodes. When using the temporal shortest distance as the distance between two nodes, the proposed model is more robust and more accurately determines the node spreading influence than baseline methods. Furthermore, when using the temporal information to quantify the mass of each node, we found that a novel robust metric can be used to accurately determine the node influence regarding both network structure and information spreading. (c) 2021 Elsevier Ltd. All rights reserved.
关键词	Temporal networks Temporal gravity model Important node Centrality
DOI	10.1016/j.chaos.2021.110934
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[11631014] ; National Natural Science Foundation of China[11871311] ; National Natural Science Foundation of China[12001324] ; China Postdoctoral Science Foundation[2019TQ0188] ; China Postdoctoral Science Foundation[2019M662315] ; Shandong University multidisciplinary research and innovation team of young scholars[2020QNQT017] ; Taishan Scholars Program Foundation of Shandong Province, China
WOS研究方向	Mathematics ; Physics
WOS类目	Mathematics, Interdisciplinary Applications ; Physics, Multidisciplinary ; Physics, Mathematical
WOS记录号	WOS:000663440300001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/58858
专题	应用数学研究所
通讯作者	Qu, Cunquan
作者单位	1.Shandong Univ, Sch Math, 27 Shanda Nanlu, Jinan 250100, Peoples R China 2.Shandong Univ, Data Sci Inst, Jinan 250100, Peoples R China 3.Delft Univ Technol, Intelligent Syst, NL-2600 GA Delft, Netherlands 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Bi, Jialin,Jin, Ji,Qu, Cunquan,et al. Temporal gravity model for important node identification in temporal networks[J]. CHAOS SOLITONS & FRACTALS,2021,147:17.
APA	Bi, Jialin,Jin, Ji,Qu, Cunquan,Zhan, Xiuxiu,Wang, Guanghui,&Yan, Guiying.(2021).Temporal gravity model for important node identification in temporal networks.CHAOS SOLITONS & FRACTALS,147,17.
MLA	Bi, Jialin,et al."Temporal gravity model for important node identification in temporal networks".CHAOS SOLITONS & FRACTALS 147(2021):17.