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arXiv · 2206.08144

A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification

Fazeleh Hoseini·Niklas Åkerblom·Morteza Haghir Chehreghani
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Abstract

Bottleneck identification is a challenging task in network analysis, especially when the network is not fully specified. To address this task, we develop a unified online learning framework based on combinatorial semi-bandits that performs bottleneck identification in parallel with learning the specifications of the underlying network. Within this framework, we adapt and study various combinatorial semi-bandit methods such as epsilon-greedy, LinUCB, BayesUCB, NeuralUCB, and Thompson Sampling. In addition, our framework is capable of using contextual information in the form of contextual bandits. Finally, we evaluate our framework on the real-world application of road networks and demonstrate its effectiveness in different settings.

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Fazeleh Hoseini, Niklas Åkerblom, Morteza Haghir Chehreghani. 2022-06-16. A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification. https://arxiv.org/abs/2206.08144

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