论文标题
影响时间网络的最大化
Influence maximization on temporal networks
论文作者
论文摘要
我们考虑在时间网络上播种扩散过程的优化问题，以便最大程度地提高所得爆发的预期大小。我们按照时间表的易感感染恢复模型的规则等于表征网络拓扑演化的一个规则，将扩散过程的问题构成问题。我们基于12个现实世界中的时间网络的语料库进行系统分析，并量化解决方案的性能，以使用有关网络拓扑和动态的不同信息获得的影响最大化问题。我们发现，对网络拓扑具有完美的了解，但是在静态和/或汇总的形式中，有效地解决了最大化问题并无助。即使是部分的知识，也是网络动力学的早期阶段的知识，而对于识别有影响力的散布器的准季节集合而言，知识也似乎是必不可少的。
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.
