We consider the optimization problem of seeding a spreading process on a temporal network so that the expected size of the resulting outbreak is maximized. We frame the problem for a spreading process following the rules of the susceptible-infected-recovered model with temporal scale equal to the one characterizing the evolution of the network topology. We perform a systematic analysis based on a corpus of 12 real-world temporal networks and quantify the performance of solutions to the influence maximization problem obtained using different level of information about network topology and dynamics. We find that having perfect knowledge of the network topology but in a static and/or aggregated form is not helpful in solving the influence maximization problem effectively. Knowledge, even if partial, of the early stages of the network dynamics appears instead essential for the identification of quasioptimal sets of influential spreaders.

中文翻译：

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对时间网络的影响最大化

我们考虑在时间网络上播种传播过程的优化问题，以使预期爆发的规模最大化。我们按照时间敏感度等于表征网络拓扑演化的时间尺度的易受感染的恢复模型的规则，对传播过程的问题进行构架。我们基于12个现实世界时态网络的语料库进行系统分析，并量化对使用网络拓扑和动力学的不同级别信息而获得的影响最大化问题的解决方案的性能。我们发现，拥有网络拓扑的完整知识，但以静态和/或聚合的形式，无助于有效地解决影响最大化问题。知识，即使是部分知识，