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Information Spreading on Weighted Multiplex Social Network

Author

Listed:
  • Xuzhen Zhu
  • Jinming Ma
  • Xin Su
  • Hui Tian
  • Wei Wang
  • Shimin Cai

Abstract

Information spreading on multiplex networks has been investigated widely. For multiplex networks, the relations of each layer possess different extents of intimacy, which can be described as weighted multiplex networks. Nevertheless, the effect of weighted multiplex network structures on information spreading has not been analyzed comprehensively. We herein propose an information spreading model on a weighted multiplex network. Then, we develop an edge-weight-based compartmental theory to describe the spreading dynamics. We discover that under any adoption threshold of two subnetworks, reducing weight distribution heterogeneity does not alter the growth pattern of the final adoption size versus information transmission probability while accelerating information spreading. For fixed weight distribution, the growth pattern changes with the heterogeneous of degree distribution. There is a critical initial seed size, below which no global information outbreak can occur. Extensive numerical simulations affirm that the theoretical predictions agree well with the numerical results.

Suggested Citation

  • Xuzhen Zhu & Jinming Ma & Xin Su & Hui Tian & Wei Wang & Shimin Cai, 2019. "Information Spreading on Weighted Multiplex Social Network," Complexity, Hindawi, vol. 2019, pages 1-15, November.
    • Handle: RePEc:hin:complx:5920187
    DOI: 10.1155/2019/5920187
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    References listed on IDEAS

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    Cited by:

    1. Li, Yang & Sun, Hao & Xiong, Wanda & Xu, Genjiu, 2021. "Belief model of complex contagions on random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    2. Lawford, Steve & Mehmeti, Yll, 2020. "Cliques and a new measure of clustering: With application to U.S. domestic airlines," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    3. Chen, Wenhao & Li, Jichao & Jiang, Jiang & Chen, Gang, 2022. "Weighted interdependent network disintegration strategy based on Q-learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).

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