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Information Spreading on Activity-Driven Temporal Networks with Two-Step Memory

Author

Listed:
  • Linfeng Zhong
  • Xiaoyu Xue
  • Yu Bai
  • Jin Huang
  • Qing Cheng
  • Longyang Huang
  • Weijun Pan
  • Chenquan Gan

Abstract

Information spreading dynamics on the temporal network is a hot topic in the field of network science. In this paper, we propose an information spreading model on an activity-driven temporal network, in which a node is accepting the information dependents on the cumulatively received pieces of information in its recent two steps. With a generalized Markovian approach, we analyzed the information spreading size, and revealed that network temporality might suppress or promote the information spreading, which is determined by the information transmission probability. Besides, the system exists a critical mass, below which the information cannot globally outbreak, and above which the information outbreak size does not change with the initial seed size. Our theory can qualitatively well predict the numerical simulations.

Suggested Citation

  • Linfeng Zhong & Xiaoyu Xue & Yu Bai & Jin Huang & Qing Cheng & Longyang Huang & Weijun Pan & Chenquan Gan, 2021. "Information Spreading on Activity-Driven Temporal Networks with Two-Step Memory," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-7, January.
    • Handle: RePEc:hin:jnddns:4506012
    DOI: 10.1155/2021/4506012
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