Information Spreading on Memory Activity-Driven Temporal Networks

Linfeng Zhong, Yu Bai, Changjiang Liu, Juan Du, Weijun Pan and Giovanni Petri

Complexity, 2021, vol. 2021, 1-8

Abstract: Information spreading dynamics on temporal networks have attracted significant attention in the field of network science. Extensive real-data analyses revealed that network memory widely exists in the temporal network. This paper proposes a mathematical model to describe the information spreading dynamics with the network memory effect. We develop a Markovian approach to describe the model. Using the Monte Carlo simulation method, we find that network memory may suppress and promote the information spreading dynamics, which depends on the degree heterogeneity and fraction of bigots. The network memory effect suppresses the information spreading for small information transmission probability. The opposite situation happens for large value of information transmission probability. Moreover, network memory effect may benefit the information spreading, which depends on the degree heterogeneity of the activity-driven network. Our results presented in this paper help us understand the spreading dynamics on temporal networks.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:8015191

DOI: 10.1155/2021/8015191

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