Improved gravity model for identifying the influential nodes

¹ Research Center of Complex Systems Science, University of Shanghai for Science and Technology Shanghai 200093, PRC
² Institute of Accounting and Finance, Shanghai University of Finance and Economics Shanghai 200433, PRC

^(a) liujg004@ustc.edu.cn (corresponding author)

Received: 29 September 2021
Accepted: 10 January 2022

Abstract

Identifying the influential nodes in a network is essential for network dynamic analysis. In this letter, inspired by the gravity model, we present an improved gravity model (EDGM) to identify the influential nodes in the network through the effective distance. Firstly, we calculate the degree of nodes. Then we construct the effective distance combined with the interaction frequency between nodes, so as to establish the effective distance gravity model. Comparing with the susceptible-infected model, the results show that Kendall's τ correlation coefficient of the EDGM could be enhanced by 2.36% for the gravity model. Compared with other methods, Kendall's τ correlation coefficient of the EDGM could be enhanced by 11.55%, 17.29%, 7.17% and 10.00% for the degree centrality, betweenness centrality, eigenvector centrality, and PageRank, respectively. The results show that the improved gravity model could effectively identify the influential nodes in the network.