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.

Author

Niklas Åkerblom

Volvo Cars

Data Science and AI

Fazeleh Sadat Hoseini

Data Science and AI

Morteza Haghir Chehreghani

Data Science and AI

EENE: Energy Effective Navigation for EVs

FFI - Strategic Vehicle Research and Innovation (2018-01937), 2019-01-01 -- 2022-12-31.

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Computer Science

Computer Vision and Robotics (Autonomous Systems)

Related datasets

arXiv:2109.08467 [dataset]

URI: https://arxiv.org/abs/2109.08467

More information

Latest update

1/20/2022