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A modified evidential methodology of identifying influential nodes in weighted networks

Author

Listed:
  • Gao, Cai
  • Wei, Daijun
  • Hu, Yong
  • Mahadevan, Sankaran
  • Deng, Yong

Abstract

How to identify influential nodes in complex networks is still an open hot issue. In the existing evidential centrality (EVC), node degree distribution in complex networks is not taken into consideration. In addition, the global structure information has also been neglected. In this paper, a new Evidential Semi-local Centrality (ESC) is proposed by modifying EVC in two aspects. Firstly, the Basic Probability Assignment (BPA) of degree generated by EVC is modified according to the actual degree distribution, rather than just following uniform distribution. BPA is the generation of probability in order to model uncertainty. Secondly, semi-local centrality combined with modified EVC is extended to be applied in weighted networks. Numerical examples are used to illustrate the efficiency of the proposed method.

Suggested Citation

  • Gao, Cai & Wei, Daijun & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2013. "A modified evidential methodology of identifying influential nodes in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5490-5500.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:21:p:5490-5500
    DOI: 10.1016/j.physa.2013.06.059
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