  Contextual bandits algorithms aim to choose the optimal arm with the highest
reward out of a set of candidates based on the contextual information. Various
bandit algorithms have been applied to real-world applications due to their
ability of tackling the exploitation-exploration dilemma. Motivated by online
recommendation scenarios, in this paper, we propose a framework named Graph
Neural Bandits (GNB) to leverage the collaborative nature among users empowered
by graph neural networks (GNNs). Instead of estimating rigid user clusters as
in existing works, we model the "fine-grained" collaborative effects through
estimated user graphs in terms of exploitation and exploration respectively.
Then, to refine the recommendation strategy, we utilize separate GNN-based
models on estimated user graphs for exploitation and adaptive exploration.
Theoretical analysis and experimental results on multiple real data sets in
comparison with state-of-the-art baselines are provided to demonstrate the
effectiveness of our proposed framework.
