Identifying influential nodes in complex networks: Effective distance gravity model

Abstract

The identification of important nodes in complex networks is an area of exciting growth due to its applications across various disciplines like disease control, data mining and network system control. Many measures have been proposed to date, but they are either based on the locality of nodes or the global nature of the network. These measures typically use the traditional Euclidean Distance, which only focuses on local static geographic distance between nodes but ignores the dynamic interaction between nodes in real-world networks. Both the static and dynamic information should be considered for the purpose of identifying influential nodes. In order to address this problem, we have proposed an original and novel gravity model with effective distance for identifying influential nodes based on information fusion and multi-level processing. Our method is able to comprehensively consider the global and local information of complex networks, and also utilizes the effective distance to incorporate static and dynamic information. Moreover, the proposed method can help us mine for hidden topological structure of real-world networks for more accurate results. The susceptible infected model, Kendall correlation coefficient and eight existing identification methods are utilized to carry out simulations on twelve different real networks.