A novel method to identify influential nodes in complex networks

Yuanzhi Yang, Lei Yu (), Xing Wang (), Siyi Chen (), You Chen and Yipeng Zhou ()
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Yuanzhi Yang: Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, P. R. China
Lei Yu: Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, P. R. China
Xing Wang: Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, P. R. China
Siyi Chen: Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, P. R. China
You Chen: Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, P. R. China
Yipeng Zhou: Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, P. R. China

International Journal of Modern Physics C (IJMPC), 2020, vol. 31, issue 02, 1-14

Abstract: Identifying influential nodes in complex networks continues to be an open and vital issue, which is of great significance to the robustness and vulnerability of networks. In order to accurately identify influential nodes in complex networks and avoid the deviation in the evaluation of node influence by single measure, a novel method based on improved Technology for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to integrate multiple measures and identify influential nodes. Our method takes into account degree centrality (DC), closeness centrality (CC) and betweenness centrality (BC), and uses the information of the decision matrix to objectively assign weight to each measure, and takes the closeness degree from each node to be the ideal solution as the basis for comprehensive evaluation. At last, four experiments based on the Susceptible-Infected (SI) model are carried out, and the superiority of our method can be demonstrated.

Keywords: Complex networks; influential nodes; centrality measure; improved TOPSIS; the SI model (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (20)

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DOI: 10.1142/S0129183120500229

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