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Identifying the influential nodes via eigen-centrality from the differences and similarities of structure

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  • Zhong, Lin-Feng
  • Shang, Ming-Sheng
  • Chen, Xiao-Long
  • Cai, Shi-Ming

Abstract

One of the most important problems in complex network is the identification of the influential nodes. For this purpose, the use of differences and similarities of structure to enrich the centrality method in complex networks is proposed. The centrality method called ECDS centrality used is the eigen-centrality which is based on the Jaccard similarities between the two random nodes. This can be described by an eigenvalues problem. Here, we use a tunable parameter α to adjust the influence of the differences and similarities. Comparing with the results of the Susceptible Infected Recovered (SIR) model for four real networks, the ECDS centrality could identify influential nodes more accurately than the tradition centralities such as the k-shell, degree and closeness centralities. Especially, in the Erdös network, the Kendall’s tau could be reached to 0.93 when the spreading rate is 0.12. In the US airline network, the Kendall’s tau could be reached to 0.95 when the spreading rate is 0.06.

Suggested Citation

  • Zhong, Lin-Feng & Shang, Ming-Sheng & Chen, Xiao-Long & Cai, Shi-Ming, 2018. "Identifying the influential nodes via eigen-centrality from the differences and similarities of structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 77-82.
    • Handle: RePEc:eee:phsmap:v:510:y:2018:i:c:p:77-82
    DOI: 10.1016/j.physa.2018.06.115
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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. 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.
    3. Bellingeri, Michele & Cassi, Davide & Vincenzi, Simone, 2014. "Efficiency of attack strategies on complex model and real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 174-180.
    4. Wang, Jie & Wang, Jun & Stanley, H. Eugene, 2018. "Multiscale multifractal DCCA and complexity behaviors of return intervals for Potts price model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 889-902.
    5. 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.
    6. Linyuan Lü & Tao Zhou & Qian-Ming Zhang & H. Eugene Stanley, 2016. "The H-index of a network node and its relation to degree and coreness," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
    7. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    8. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
    9. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    10. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
    11. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    12. Shu-Jiao Ma & Zhuo-Ming Ren & Chun-Ming Ye & Qiang Guo & Jian-Guo Liu, 2014. "Node influence identification via resource allocation dynamics," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 25(11), pages 1-13.
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    Cited by:

    1. Ai, Jun & He, Tao & Su, Zhan, 2023. "Identifying influential nodes in complex networks based on resource allocation similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 627(C).
    2. Wang, Jie & Wang, Jun, 2020. "Cross-correlation complexity and synchronization of the financial time series on Potts dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    3. Fei Ma & Yujie Zhu & Kum Fai Yuen & Qipeng Sun & Haonan He & Xiaobo Xu & Zhen Shang & Yan Xu, 2022. "Exploring the Spatiotemporal Evolution and Sustainable Driving Factors of Information Flow Network: A Public Search Attention Perspective," IJERPH, MDPI, vol. 19(1), pages 1-25, January.
    4. Wen, Xing-Zhang & Zheng, Yue & Du, Wen-Li & Ren, Zhuo-Ming, 2023. "Regulating clustering and assortativity affects node centrality in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).

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