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.

中文翻译：

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记忆活动驱动的时间网络上的信息传播

时间网络上的信息传播动态在网络科学领域引起了极大的关注。大量的真实数据分析表明，网络记忆广泛存在于时间网络中。本文提出了一个数学模型来描述具有网络记忆效应的信息传播动态。我们开发了一种马尔可夫方法来描述模型。使用蒙特卡罗模拟方法，我们发现网络记忆可能会抑制和促进信息传播动态，这取决于异质性的程度和偏执者的比例。网络记忆效应抑制了信息传播概率较小的信息传播。相反的情况发生在大的信息传输概率值上。此外，网络记忆效应可能有利于信息的传播，这取决于活动驱动网络的程度异质性。我们在本文中提出的结果帮助我们了解时间网络上的传播动态。