A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification
This work addresses network analysis challenges for domains like transportation, but it appears incremental as it adapts existing bandit methods to a specific task.
The paper tackled the problem of bottleneck identification in networks with incomplete specifications by developing an online learning framework based on combinatorial semi-bandits, which adapts methods like epsilon-greedy and Thompson Sampling and shows effectiveness in road network applications.
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.