Spreading dynamics has been considered to take place in temporal networks, where temporal interaction patterns between nodes show non-Poissonian bursty nature. The effects of inhomogeneous interevent times (IETs) on the spreading have been extensively studied in recent years, yet little is known about the effects of correlations between IETs on the spreading. In order to investigate those effects, we study two-step deterministic susceptible-infected (SI) and probabilistic SI dynamics when the interaction patterns are modeled by inhomogeneous and correlated IETs, i.e., correlated bursts. By analyzing the transmission time statistics in a single-link setup and by simulating the spreading in Bethe lattices and random graphs, we conclude that the positive correlation between IETs slows down the spreading. We also argue that the shortest transmission time from one infected node to its susceptible neighbors can successfully explain our numerical results.

中文翻译：

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时态网络中的相关突发会减慢传播速度。

扩展动力学已经被认为发生在时间网络中，其中节点之间的时间交互模式显示出非泊松突发性。近年来，人们对不均匀的事件间隔时间（IET）对传播的影响进行了广泛的研究，但对IET之间的相关性对传播的影响知之甚少。为了研究这些影响，当交互模式由不均匀和相关的IET（即相关的爆发）建模时，我们研究了两步确定性易感感染（SI）和概率SI动力学。通过分析单链路设置中的传输时间统计数据，并通过模拟Bethe晶格和随机图中的扩展，我们得出结论，IET之间的正相关会减慢扩展。