  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.
