Temporal gravity model for important node identification in temporal networks

Jialin Bi, Ji Jin, Cunquan Qu, Xiuxiu Zhan, Guanghui Wang, Guiying Yan

Research output: Contribution to journalArticleScientificpeer-review

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.

Original languageEnglish
Article number110934
JournalChaos, Solitons and Fractals
Volume147
DOIs
Publication statusPublished - 2021

Keywords

  • Centrality
  • Important node
  • Temporal gravity model
  • Temporal networks

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