Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, Hunan 411105, China.
School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, Q4001, Australia.
Sci Rep. 2017 Feb 2;7:41454. doi: 10.1038/srep41454.
Locating influential nodes in temporal networks has attracted a lot of attention as data driven and diverse applications. Classic works either looked at analysing static networks or placed too much emphasis on the topological information but rarely highlighted the dynamics. In this paper, we take account the network dynamics and extend the concept of Dynamic-Sensitive centrality to temporal network. According to the empirical results on three real-world temporal networks and a theoretical temporal network for susceptible-infected-recovered (SIR) models, the temporal Dynamic-Sensitive centrality (TDC) is more accurate than both static versions and temporal versions of degree, closeness and betweenness centrality. As an application, we also use TDC to analyse the impact of time-order on spreading dynamics, we find that both topological structure and dynamics contribute the impact on the spreading influence of nodes, and the impact of time-order on spreading influence will be stronger when spreading rate b deviated from the epidemic threshold b, especially for the temporal scale-free networks.
在时间网络中定位有影响力的节点引起了广泛关注，因为它具有数据驱动和多样化的应用。经典作品要么研究分析静态网络，要么过于强调拓扑信息，但很少强调动态。在本文中，我们考虑了网络动态，并将动态敏感中心度的概念扩展到了时间网络中。根据三个真实时间网络和一个易感感染恢复（SIR）模型的理论时间网络的实证结果，时间动态敏感中心度（TDC）比度、接近度和中间中心度的静态和时间版本都更准确。作为一个应用，我们还使用 TDC 来分析时间顺序对传播动态的影响，我们发现拓扑结构和动态都对节点的传播影响有贡献，并且当传播率 b 偏离流行阈值 b 时，时间顺序对传播影响的贡献更大，特别是对于时间无标度网络。
