We present a collection of sampling-based algorithms for approximating the temporal betweenness centrality of all nodes in a temporal graph. Our methods can compute probabilistically guaranteed high-quality temporal betweenness estimates (of nodes and temporal edges) under all the feasible temporal path optimalities presented in the work of Bu{\ss} et al. (KDD, 2020). We provide a sample-complexity analysis of these methods and we speed up the temporal betweenness computation using progressive sampling techniques. Finally, we conduct an extensive experimental evaluation on real-world networks and we compare their performances in approximating the betweenness scores and rankings.

翻译：我们提出了一系列基于抽样的算法，用于近似计算时间图中所有节点的时间介数中心性。我们的方法能够在Bu{\ss}等人（KDD, 2020）工作中提出的所有可行时间路径最优性下，计算具有概率保证的高质量时间介数估计（包括节点和时间边）。我们提供了这些方法的样本复杂度分析，并通过渐进抽样技术加速了时间介数计算。最后，我们在真实网络数据集上进行了广泛的实验评估，并比较了它们在近似介数分数和排名方面的性能。