title: Locating influential nodes via dynamics-sensitive centrality.
abstract: With great theoretical and practical significance, locating influential nodes of complex networks is a promising issue. In this paper, we present a dynamics-sensitive (DS) centrality by integrating 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 more accurate than degree, k-shell index and eigenvector centrality.
authors.affiliation: Data Science and Cloud Service Centre, Shanghai University of Finance and Economics, Shanghai 200433, PR China.|Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, PR China.
authors.forename: Jian-Guo
authors.initials: JG
authors.lastname: Liu
authors.affiliation: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, PR China.
authors.forename: Jian-Hong
authors.initials: JH
authors.lastname: Lin
authors.affiliation: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, PR China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Guo
authors.affiliation: CompleX Lab, Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, PR China.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhou