Identifying influential nodes in complex networks

Duanbing Chen, Linyuan Lü, Ming-Sheng Shang, Yi-Cheng Zhang and Tao Zhou

Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, issue 4, 1777-1787

Abstract: Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational complexity. In order to design an effective ranking method, we proposed a semi-local centrality measure as a tradeoff between the low-relevant degree centrality and other time-consuming measures. We use the Susceptible–Infected–Recovered (SIR) model to evaluate the performance by using the spreading rate and the number of infected nodes. Simulations on four real networks show that our method can well identify influential nodes.

Keywords: Complex networks; Centrality measures; Influential nodes; Spreading; SIR model (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (141)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:391:y:2012:i:4:p:1777-1787

DOI: 10.1016/j.physa.2011.09.017

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