2015/05/31 by Ling-Ling Ma, Ling-ling Ma, Chuang Ma +3
Computer Science · Mathematics · Physics and Astronomy · Social Sciences · #Combinatorics #Complex Network Analysis Techniques #Complex network #Computer science #Evolutionary Game Theory and Cooperation #Mathematics #Opinion Dynamics and Social Influence #Physics #Statistical physics #cs.SI #physics.soc-ph
paper · pdf · doi:10.1016/j.physa.2015.12.162
published as Physica A, 451(1), 205-212, (2016) · 4 tables and 4 figures, accepted by Physica A
openalex publication_date 2016/01/29 · arxiv created 2016/03/29 · arxiv updated 2016/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
How to identify the influential spreaders in social networks is crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases and rumors, and so on. In this paper, by viewing the k-shell value of each node as its mass and the shortest path distance between two nodes as their distance, then inspired by the idea of the gravity formula, we propose a gravity centrality index to identify the influential spreaders in complex networks. The comparison between the gravity centrality index and some well-known centralities, such as degree centrality, betweenness centrality, closeness centrality, and k-shell centrality, and so forth, indicates that our method can effectively identify the influential spreaders in real networks as well as synthetic networks. We also use the classical Susceptible-Infected-Recovered (SIR) epidemic model to verify the good performance of our method.