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Temporal gravity model for important node identification in temporal networks
Bi, Jialin1; Jin, Ji1; Qu, Cunquan1,2; Zhan, Xiuxiu3; Wang, Guanghui1,2; Yan, Guiying4,5
AbstractIdentifying 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.
KeywordTemporal networks Temporal gravity model Important node Centrality
Indexed BySCI
Funding ProjectNational 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 Research AreaMathematics ; Physics
WOS SubjectMathematics, Interdisciplinary Applications ; Physics, Multidisciplinary ; Physics, Mathematical
WOS IDWOS:000663440300001
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Document Type期刊论文
Corresponding AuthorQu, Cunquan
Affiliation1.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
Recommended Citation
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.
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