2015/04/25 by Jian-Hong Lin, Jianhong Lin, Lin, Jian-Hong +6 · 1 citation
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Opinion Dynamics and Social Influence #Opportunistic and Delay-Tolerant Networks #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistics and Probability (physics.data-an) #cs.SI #physics.data-an #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1504.06672
6 pages, 1 table and 2 figures
arxiv created 2015/04/25 · openalex publication_date 2015/04/25 · arxiv updated 2015/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With great theoretical and practical significance, locating influential nodes of complex networks is a promising issues. In this paper, we propose a dynamics-sensitive (DS) centrality that integrates topological features and dynamical properties. The DS centrality can be directly applied in locating influential spreaders. According to the empirical results on four real networks for both susceptible-infected-recovered (SIR) and susceptible-infected (SI) spreading models, the DS centrality is much more accurate than degree, k-shell index and eigenvector centrality.