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Evolving model of weighted networks inspired by scientific collaboration networks

Author

Listed:
  • Li, Menghui
  • Wu, Jinshan
  • Wang, Dahui
  • Zhou, Tao
  • Di, Zengru
  • Fan, Ying

Abstract

Inspired by scientific collaboration networks (SCN), especially our empirical analysis of econophysicists network, an evolutionary model for weighted networks is proposed. Besides a new vertex added in at every time step, old vertices can also attempt to build up new links, or to reconnect the existing links. The number of connections repeated between two nodes is converted into the weight of the link. This provides a natural way for the evolution of link weight. The path-dependent preferential attachment mechanism with local information is also introduced. It increases the clustering coefficient of the network significantly. The model shows the scale-free phenomena in degree and vertex weight distribution. It also gives well qualitatively consistent behavior with the empirical results.

Suggested Citation

  • Li, Menghui & Wu, Jinshan & Wang, Dahui & Zhou, Tao & Di, Zengru & Fan, Ying, 2007. "Evolving model of weighted networks inspired by scientific collaboration networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(1), pages 355-364.
    • Handle: RePEc:eee:phsmap:v:375:y:2007:i:1:p:355-364
    DOI: 10.1016/j.physa.2006.08.023
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    Citations

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    Cited by:

    1. Andrea Fronzetti Colladon & Ciriaco Andrea D’Angelo & Peter A. Gloor, 2020. "Predicting the future success of scientific publications through social network and semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 357-377, July.
    2. Türker, İlker & Çavuşoğlu, Abdullah, 2016. "Detailing the co-authorship networks in degree coupling, edge weight and academic age perspective," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 386-392.
    3. Stanislaw Drozdz & Andrzej Kulig & Jaroslaw Kwapien & Artur Niewiarowski & Marek Stanuszek, 2017. "Hierarchical organization of H. Eugene Stanley scientific collaboration community in weighted network representation," Papers 1705.06208, arXiv.org, revised Oct 2017.
    4. Lemarchand, Guillermo A., 2012. "The long-term dynamics of co-authorship scientific networks: Iberoamerican countries (1973–2010)," Research Policy, Elsevier, vol. 41(2), pages 291-305.
    5. Huang, He & Yan, Zhijun & Pan, Yaohui, 2014. "Measuring edge importance to improve immunization performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 532-540.
    6. Chen, Xue & Jiao, Pengfei & Yu, Yandong & Li, Xiaoming & Tang, Minghu, 2019. "Toward link predictability of bipartite networks based on structural enhancement and structural perturbation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C), pages 1-1.
    7. Zhang, Xin-Jie & Tang, Yong & Xiong, Jason & Wang, Wei-Jia & Zhang, Yi-Cheng, 2020. "Ranking game on networks: The evolution of hierarchical society," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    8. Sousa, R.A. & Lula-Rocha, V.N.A. & Toutain, T. & Rosário, R.S. & Cambui, E.C.B. & Miranda, J.G.V., 2020. "Preferential interaction networks: A dynamic model for brain synchronization networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).

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