Learning from Local Walks on Dynamic Graphs with Bandit Feedback
We study stochastic multi-armed bandits on dynamic graphs, where arms correspond to the vertices of a network with time-varying edges. In this setting, the learner is restricted to local movement, selecting only its current node or an immediate neighbor at each round. This constraint decouples best-arm identification from exploitation: even after the optimal arm is identified, the learner may remain unable to reach it through the evolving topology. We identify a process-agnostic structural condition, based on sliding-window mixing, that ensures the graph's intrinsic walk remains stable for both exploration and navigation. Under this regime, we analyze a family of local explore-then-commit...
| Authors | Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 July 2026 |
| Last updated at source | 27 July 2026 |
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