Temporal gravity model for important node identification in temporal networks
文献类型：期刊论文
| 作者 | Bi, Jialin5; Jin, Ji5; Qu, Cunquan4,5; Zhan, Xiuxiu3; Wang, Guanghui4,5; Yan, Guiying1,2
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| 刊名 | CHAOS SOLITONS & FRACTALS
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| 出版日期 | 2021-06-01 |
| 卷号 | 147页码:17 |
| 关键词 | Temporal networks Temporal gravity model Important node Centrality |
| ISSN号 | 0960-0779 |
| DOI | 10.1016/j.chaos.2021.110934 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000663440300001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/58857]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Qu, Cunquan |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 3.Delft Univ Technol, Intelligent Syst, NL-2600 GA Delft, Netherlands 4.Shandong Univ, Data Sci Inst, Jinan 250100, Peoples R China 5.Shandong Univ, Sch Math, 27 Shanda Nanlu, Jinan 250100, 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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