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Ranking the spreading influence in complex networks

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  • Liu, Jian-Guo
  • Ren, Zhuo-Ming
  • Guo, Qiang

Abstract

Identifying the node spreading influence in 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 shortest distance between a target node and the node set with the highest k-core value, we present an improved method to generate the ranking list to evaluate the node spreading influence. Comparing with the epidemic process results for four real networks and the Barabási–Albert network, the parameterless method could identify the node spreading influence more accurately than the ones generated by the degree k, closeness centrality, k-shell and mixed degree decomposition methods. This work would be helpful for deeply understanding the node importance of a network.

Suggested Citation

  • Liu, Jian-Guo & Ren, Zhuo-Ming & Guo, Qiang, 2013. "Ranking the spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4154-4159.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:18:p:4154-4159
    DOI: 10.1016/j.physa.2013.04.037
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    References listed on IDEAS

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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Jian-Guo Liu & Zhi-Xi Wu & Feng Wang, 2007. "Opinion Spreading And Consensus Formation On Square Lattice," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(07), pages 1087-1094.
    3. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
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