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On the effects of capability and popularity on network dynamics with applications to YouTube and Twitch networks

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  • Jung, Hohyun
  • Phoa, Frederick Kin Hing

Abstract

The popularity effect in a network, i.e., the phenomenon in which popular nodes become more popular, has been explained through the fitness models considering the node heterogeneity. The “attractive” nodes, i.e., nodes with large fitness, are likely to have high popularity. The indegree has been regarded as node popularity. When popularity is not given in the form of indegree, however, the interaction between fitness and popularity may not be considered. In this study, we generalize the concept of fitness to capability and propose a capability–popularity dynamic network (CPDN) model. The CPDN model considers the interaction between the popularity and node heterogeneity when popularity is not expressed in indegree. Broad popularity and indegree processes can be covered in the framework of the proposed model. We present the EM algorithm combined with a Bayesian inference method to infer the node capability and model parameters. Monte Carlo simulations are performed to show the validity of the CPDN model. We analyze the Twitch following and YouTube subscription networks and examine how the popularity effect works in the network growth with remarkable interpretations.

Suggested Citation

  • Jung, Hohyun & Phoa, Frederick Kin Hing, 2021. "On the effects of capability and popularity on network dynamics with applications to YouTube and Twitch networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    • Handle: RePEc:eee:phsmap:v:571:y:2021:i:c:s0378437120309614
    DOI: 10.1016/j.physa.2020.125663
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    References listed on IDEAS

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    Cited by:

    1. Jung, Hohyun, 2023. "Eliminating the biases of user influence and item popularity in bipartite networks: A case study of Flickr and Netflix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).

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