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.
Highlights
Extensive real-data analyses revealed that social network exhibits strong temporal properties [1,2,3], i.e., the edges and nodes do not always exist at any time, and may vary with time
We propose a mathematical model on temporal networks with memory. en, we develop a Markovian theory for the dynamical model. rough extensive Monte Carlo simulations, we systematically investigate the dynamics
We proposed a mathematical model to investigate the effects of network memory on the information spreading dynamics on temporal networks
Summary
Extensive real-data analyses revealed that social network exhibits strong temporal properties [1,2,3], i.e., the edges and nodes do not always exist at any time, and may vary with time. Researchers further revealed that the network community, clustering, and degree-degree correlations could alter the spreading dynamics of information [26, 27]. For that threshold-based information spreading model, the phase transition of the dynamical system is always discontinuous, i.e., first-order phase transition [28]. Complexity proposed nonredundant information spreading dynamics and revealed a transition between the continuous and discontinuous transition in the system.
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