Influential nodes identification in complex networks based on global and local information

doi:10.1088/1674-1056/ab969f

Abstract Identifying influential nodes in complex networks is essential for network robust and stability, such as viral marketing and information control. Various methods have been proposed to define the influence of nodes. In this paper, we comprehensively consider the global position and local structure to identify influential nodes. The number of iterations in the process of k-shell decomposition is taken into consideration, and the improved k-shell decomposition is then put forward. The improved k-shell decomposition and degree of target node are taken as the benchmark centrality, in addition, as is well known, the effect between node pairs is inversely proportional to the shortest path length between two nodes, and then we also consider the effect of neighbors on target node. To evaluate the performance of the proposed method, susceptible-infected (SI) model is adopted to simulate the spreading process in four real networks, and the experimental results show that the proposed method has obvious advantages over classical centrality measures in identifying influential nodes.

Keywords: complex networks influential nodes global position local structure susceptible-infected (SI) model

Received: 23 April 2020
Revised: 18 May 2020
Accepted manuscript online:

PACS:

89.75.Fb

(Structures and organization in complex systems)

Corresponding Authors: Min Hu, Min Hu
E-mail: hu_min_min@163.com;1097762865@qq.com

Cite this article:

Yuan-Zhi Yang(杨远志), Min Hu(胡敏), Tai-Yu Huang(黄泰愚) Influential nodes identification in complex networks based on global and local information 2020 Chin. Phys. B 29 088903

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