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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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    1. repec:eee:phsmap:v:490:y:2018:i:c:p:1061-1075 is not listed on IDEAS
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Konstadinos G. Goulias & Ram M. Pendyala, 2014. "Choice context," Chapters,in: Handbook of Choice Modelling, chapter 5, pages 101-130 Edward Elgar Publishing.
    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. 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.
    9. König, Michael David, 2016. "The formation of networks with local spillovers and limited observability," Theoretical Economics, Econometric Society, vol. 11(3), September.
    10. 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.

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