Node influence identification via resource allocation dynamics

Shu-Jiao Ma, Zhuo-Ming Ren, Chun-Ming Ye, Qiang Guo and Jian-Guo Liu ()
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Shu-Jiao Ma: Business School, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Zhuo-Ming Ren: Business School, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Chun-Ming Ye: Business School, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Qiang Guo: Business School, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China
Jian-Guo Liu: Business School, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China

International Journal of Modern Physics C (IJMPC), 2014, vol. 25, issue 11, 1-13

Abstract: Identifying the node influence in complex networks is an important task to optimally use the network structure and ensure the more efficient spreading in information. In this paper, by taking into account the resource allocation dynamics (RAD) and thek-shell decomposition method, we present an improved method namely RAD to generate the ranking list to evaluate the node influence. First, comparing with the epidemic process results for four real networks, the RAD method could identify the node influence more accurate than the ones generated by the topology-based measures including the degree,k-shell, closeness and the betweenness. Then, a growing scale-free network model with tunable assortative coefficient is introduced to analyze the effect of the assortative coefficient on the accuracy of the RAD method. Finally, the positive correlation is found between the RAD method and thek-shell values which display an exponential form. This work would be helpful for deeply understanding the node influence of a network.

Keywords: Resource allocation dynamics; k-shell; node influence; complex networks; 89.75.Fb; 89.75.Hc; 05.10.-a (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S012918311450065X

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