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Collective iteration behavior for online social networks

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  • Liu, Jian-Guo
  • Li, Ren-De
  • Guo, Qiang
  • Zhang, Yi-Cheng

Abstract

Understanding the patterns of collective behavior in online social network (OSNs) is critical to expanding the knowledge of human behavior and tie relationship. In this paper, we investigate a specific pattern called social signature in Facebook and Wiki users’ online communication behaviors, capturing the distribution of frequency of interactions between different alters over time in the ego network. The empirical results show that there are robust social signatures of interactions no matter how friends change over time, which indicates that a stable commutation pattern exists in online communication. By comparing a random null model, we find the that commutation pattern is heterogeneous between ego and alters. Furthermore, in order to regenerate the pattern of the social signature, we present a preferential interaction model, which assumes that new users intend to look for the old users with strong ties while old users have tendency to interact with new friends. The experimental results show that the presented model can reproduce the heterogeneity of social signature by adjusting 2 parameters, the number of communicating targets m and the max number of interactions n, for Facebook users, m=n=5, for Wiki users, m=2 and n=8. This work helps in deeply understanding the regularity of social signature.

Suggested Citation

  • Liu, Jian-Guo & Li, Ren-De & Guo, Qiang & Zhang, Yi-Cheng, 2018. "Collective iteration behavior for online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 490-497.
    • Handle: RePEc:eee:phsmap:v:499:y:2018:i:c:p:490-497
    DOI: 10.1016/j.physa.2018.02.069
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    Cited by:

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    3. Meng, Yanhong & Yi, Yunhui & Xiong, Fei & Pei, Changxing, 2019. "T×oneHop approach for dynamic influence maximization problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 575-586.

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