Print Email Facebook Twitter Temporal gravity model for important node identification in temporal networks Title Temporal gravity model for important node identification in temporal networks Author Bi, Jialin (Shandong University) Jin, Ji (Shandong University) Qu, Cunquan (Shandong University) Zhan, X. (TU Delft Intelligent Systems; TU Delft Multimedia Computing) Wang, Guanghui (Shandong University) Yan, Guiying (Chinese Academy of Sciences; University of Chinese Academy of Sciences) Department Intelligent Systems Date 2021 Abstract 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. Subject CentralityImportant nodeTemporal gravity modelTemporal networks To reference this document use: http://resolver.tudelft.nl/uuid:ccc05f44-906d-4b83-9bce-82ae589b1012 DOI https://doi.org/10.1016/j.chaos.2021.110934 Embargo date 2021-10-31 ISSN 0960-0779 Source Chaos, Solitons & Fractals, 147 Bibliographical note Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2021 Jialin Bi, Ji Jin, Cunquan Qu, X. Zhan, Guanghui Wang, Guiying Yan Files PDF 1_s2.0_S0960077921002885_main.pdf 2.34 MB Close viewer /islandora/object/uuid:ccc05f44-906d-4b83-9bce-82ae589b1012/datastream/OBJ/view