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The data-driven null models for information dissemination tree in social networks

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  • Zhang, Zhiwei
  • Wang, Zhenyu

Abstract

For the purpose of detecting relatedness and co-occurrence between users, as well as the distribution features of nodes in spreading path of a social network, this paper explores topological characteristics of information dissemination trees (IDT) that can be employed indirectly to probe the information dissemination laws within social networks. Hence, three different null models of IDT are presented in this article, including the statistical-constrained 0-order IDT null model, the random-rewire-broken-edge 0-order IDT null model and the random-rewire-broken-edge 2-order IDT null model. These null models firstly generate the corresponding randomized copy of an actual IDT; then the extended significance profile, which is developed by adding the cascade ratio of information dissemination path, is exploited not only to evaluate degree correlation of two nodes associated with an edge, but also to assess the cascade ratio of different length of information dissemination paths. The experimental correspondences of the empirical analysis for several SinaWeibo IDTs and Twitter IDTs indicate that the IDT null models presented in this paper perform well in terms of degree correlation of nodes and dissemination path cascade ratio, which can be better to reveal the features of information dissemination and to fit the situation of real social networks.

Suggested Citation

  • Zhang, Zhiwei & Wang, Zhenyu, 2017. "The data-driven null models for information dissemination tree in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 394-411.
    • Handle: RePEc:eee:phsmap:v:484:y:2017:i:c:p:394-411
    DOI: 10.1016/j.physa.2017.05.008
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    References listed on IDEAS

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    1. Zhang, Zhiwei & Wang, Zhenyu, 2015. "Mining overlapping and hierarchical communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 25-33.
    2. Yi, Chengqi & Bao, Yuanyuan & Xue, Yibo, 2016. "Mining the key predictors for event outbreaks in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 247-260.
    3. Maslov, Sergei & Sneppen, Kim & Zaliznyak, Alexei, 2004. "Detection of topological patterns in complex networks: correlation profile of the internet," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 529-540.
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