We study network centrality measures that take into account the specific structure of networks with time-stamped edges. In particular, we explore how such measures can be used to identify nodes most relevant for the spread of epidemics on directed, temporal contact networks. We present a percolation study on the French cattle trade network, proving that time-aware centrality measures such as the TempoRank significantly outperform measures defined on the static network. In order to make TempoRank amenable to large-scale networks, we show how it can be efficiently computed through direct simulation of time-respecting random walks.

中文翻译：

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牛贸易网络的动态集中度措施

我们研究考虑到带有时间戳边缘的网络的特定结构的网络中心性度量。特别是，我们探索如何使用这些措施来识别与有向，暂时的联系网络上的流行病传播最相关的节点。我们对法国的养牛贸易网络进行了渗滤研究，证明了诸如TempoRank之类的具有时间意识的集中度指标明显优于静态网络上定义的指标。为了使TempoRank适应大型网络，我们展示了如何通过对时间相关的随机游走进行直接仿真来有效地计算出它。