In this paper, we study bottleneck identification in networks via extracting minimax paths. Many real-world networks have stochastic weights for which full knowledge is not available in advance. Therefore, we model this task as a combinatorial semi-bandit problem to which we apply a combinatorial version of Thompson Sampling and establish an upper bound on the corresponding Bayesian regret. Due to the computational intractability of the problem, we then devise an alternative problem formulation which approximates the original objective. Finally, we experimentally evaluate the performance of Thompson Sampling with the approximate formulation on real-world directed and undirected networks.

翻译：在本文中,我们研究网络中的瓶颈识别,通过提取迷你麦克斯路径。许多现实世界网络具有不完全事先掌握知识的随机权重。因此,我们将这项任务作为组合半山地问题进行模拟,对此我们采用Thompson抽样的组合版,并在相应的巴伊西亚遗憾上设定一个上限。由于这一问题的计算性能不易,我们然后设计出一种与最初目标相近的替代问题配方。最后,我们实验性地评估Thompson抽样的性能与现实世界定向和非定向网络的近似配方。