# A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification

arXiv: https://arxiv.org/abs/2206.08144 · HTML: https://mlsanity.com/paper/2206.08144/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Learning to Optimize Via Posterior Sampling](https://mlsanity.com/paper/1301.2609/) (2013)
- [Finite-Time Analysis of Kernelised Contextual Bandits](https://mlsanity.com/paper/1309.6869/) (2013)
- [Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling](https://mlsanity.com/paper/1802.09127/) (2018)
- [Neural Thompson Sampling](https://mlsanity.com/paper/2010.00827/) (2020)
- [Thompson Sampling for Combinatorial Semi-Bandits](https://mlsanity.com/paper/1803.04623/) (2018)
- [EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits](https://mlsanity.com/paper/2110.03177/) (2021)
- [Online Learning of Network Bottlenecks via Minimax Paths](https://mlsanity.com/paper/2109.08467/) (2021)
- [An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles](https://mlsanity.com/paper/2003.01416/) (2020)

## Built on by (most cited first, 2 of 2)

- [Tree Ensembles for Contextual Bandits](https://mlsanity.com/paper/2402.06963/) (2024)
- [Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation](https://mlsanity.com/paper/2607.13686/) (2026)

## Abstract (arXiv)

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.