Online Learning of Network Bottlenecks via Minimax Paths
Preprint, 2021

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.


Niklas Åkerblom

Volvo Cars

Data Science och AI

Fazeleh Sadat Hoseini

Data Science och AI

Morteza Haghir Chehreghani

Data Science och AI


Informations- och kommunikationsteknik



Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)

Relaterade dataset

arXiv:2109.08467 [dataset]


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