Identifying influential spreaders by gravity model

• Published in: Scientific reports Vol. 9; no. 1; p. 8387
• Main Authors: Li, Zhe, Ren, Tao, Ma, Xiaoqi, Liu, Simiao, Zhang, Yixin, Zhou, Tao
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.06.2019; Nature Publishing Group
• Subjects: 639/766/530/2801; 639/766/530/2804; Algorithms; Collaboration; Computer applications; Epidemics; Gravity; Humanities and Social Sciences; Influence; Information processing; multidisciplinary; Neighborhoods; Science; Science (multidisciplinary); Social networks; Spreading
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-44930-9

Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and path information to measure a node’s importance in spreading dynamics. In order to reduce the accumulated errors caused by interactions at distance and to lower the computational complexity, a local version of the gravity model is further proposed by introducing a truncation radius. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on fourteen real networks show that the gravity model and the local gravity model perform very competitively in comparison with well-known state-of-the-art methods. For the local gravity model, the empirical results suggest an approximately linear relation between the optimal truncation radius and the average distance of the network.