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A model for social networks

Author

Listed:
  • Toivonen, Riitta
  • Onnela, Jukka-Pekka
  • Saramäki, Jari
  • Hyvönen, Jörkki
  • Kaski, Kimmo

Abstract

Social networks are organized into communities with dense internal connections, giving rise to high values of the clustering coefficient. In addition, these networks have been observed to be assortative, i.e., highly connected vertices tend to connect to other highly connected vertices, and have broad degree distributions. We present a model for an undirected growing network which reproduces these characteristics, with the aim of producing efficiently very large networks to be used as platforms for studying sociodynamic phenomena. The communities arise from a mixture of random attachment and implicit preferential attachment. The structural properties of the model are studied analytically and numerically, using the k-clique method for quantifying the communities.

Suggested Citation

  • Toivonen, Riitta & Onnela, Jukka-Pekka & Saramäki, Jari & Hyvönen, Jörkki & Kaski, Kimmo, 2006. "A model for social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 851-860.
    • Handle: RePEc:eee:phsmap:v:371:y:2006:i:2:p:851-860
    DOI: 10.1016/j.physa.2006.03.050
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    Citations

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    Cited by:

    1. Mattia G. Bergomi & Massimo Ferri & Pietro Vertechi & Lorenzo Zuffi, 2021. "Beyond Topological Persistence: Starting from Networks," Mathematics, MDPI, vol. 9(23), pages 1-15, November.
    2. Fu, Zhaohao & Hao, Lingxin, 2018. "Agent-based modeling of China’s rural–urban migration and social network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1061-1075.
    3. Ikeda, Nobutoshi, 2021. "Stratified structure of fractal scale-free networks generated by local rules," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    4. Lv, Yanhua & Ding, Ying & Song, Min & Duan, Zhiguang, 2018. "Topology-driven trend analysis for drug discovery," Journal of Informetrics, Elsevier, vol. 12(3), pages 893-905.
    5. Yuan, Wei-Guo & Liu, Yun, 2015. "A mixing evolution model for bidirectional microblog user networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 167-179.
    6. Benjamin J Finley & Kalevi Kilkki, 2014. "Exploring Empirical Rank-Frequency Distributions Longitudinally through a Simple Stochastic Process," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-14, April.
    7. Johansson, Tobias, 2017. "Gossip spread in social network Models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 126-134.
    8. Luthi, Leslie & Pestelacci, Enea & Tomassini, Marco, 2008. "Cooperation and community structure in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 955-966.
    9. Konstadinos G. Goulias & Ram M. Pendyala, 2014. "Choice context," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 5, pages 101-130, Edward Elgar Publishing.
    10. Postigo-Boix, Marcos & Melús-Moreno, José L., 2018. "A social model based on customers’ profiles for analyzing the churning process in the mobile market of data plans," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 571-592.
    11. Tomassini, Marco & Luthi, Leslie, 2007. "Empirical analysis of the evolution of a scientific collaboration network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(2), pages 750-764.
    12. Lublóy, Ágnes & Szenes, Márk, 2007. "Az ügyfélelvándorlás kereskedelmi banki modellezése [Modelling the migration of commercial bank clients]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(10), pages 915-934.
    13. Ikeda, Nobutoshi, 2010. "Impact of initial lattice structures on networks generated by traces of random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3336-3347.
    14. Zhou, Bin & Yan, Xiao-Yong & Xu, Xiao-Ke & Xu, Xiao-Ting & Wang, Nianxin, 2018. "Evolutionary of online social networks driven by pareto wealth distribution and bidirectional preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 427-434.
    15. Ikeda, Nobutoshi, 2015. "Effects of triad formations stimulated by intermediaries on network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 897-908.
    16. Guan, Yuan-Pan & You, Zhi-Qiang & Han, Xiao-Pu, 2016. "Reconstruction of social group networks from friendship networks using a tag-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 485-492.
    17. , David, 2016. "The formation of networks with local spillovers and limited observability," Theoretical Economics, Econometric Society, vol. 11(3), September.
    18. Irene Crimaldi & Michela Del Vicario & Greg Morrison & Walter Quattrociocchi & Massimo Riccaboni, 2015. "Homophily and Triadic Closure in Evolving Social Networks," Working Papers 3/2015, IMT School for Advanced Studies Lucca, revised May 2015.
    19. Zhou, Bin & Xu, Xiao-Ting & Liu, Jian-Guo & Xu, Xiao-Ke & Wang, Nianxin, 2019. "Information interaction model for the mobile communication networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1170-1176.

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