  In this paper, we investigate the stochastic contextual bandit with general
function space and graph feedback. We propose an algorithm that addresses this
problem by adapting to both the underlying graph structures and reward gaps. To
the best of our knowledge, our algorithm is the first to provide a
gap-dependent upper bound in this stochastic setting, bridging the research gap
left by the work in [35]. In comparison to [31,33,35], our method offers
improved regret upper bounds and does not require knowledge of graphical
quantities. We conduct numerical experiments to demonstrate the computational
efficiency and effectiveness of our approach in terms of regret upper bounds.
These findings highlight the significance of our algorithm in advancing the
field of stochastic contextual bandits with graph feedback, opening up avenues
for practical applications in various domains.
