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# Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits

*Proceedings of The 35th Uncertainty in Artificial Intelligence Conference*, PMLR 115:595-605, 2020.

#### Abstract

We study the classical linear bandit problem on \emph{graphs} modelling arm rewards through an underlying graph structure $G$($N$,$E$) such that rewards of neighboring nodes are similar. Previous attempts along this line have primarily considered the arm rewards to be a smooth function over graph Laplacian, which however failed to characterize the inherent problem complexity in terms of the graph structure. We bridge this gap by showing a regret guarantee of $\tO(\chi(\overline{G})\sqrt{T})$ ($\tO(\cdot)$ notation hides dependencies on $\log T$), that scales only with the chromatic number of the complement graph $\chi(\overline{G})$, assuming the rewards to be a smooth function over a general class of graph embeddings—\emph{Orthonormal Representations}. Our proposed algorithms yield a regret guarantee of $\tilde O(r\sqrt T)$ for any general embedding of rank $r$. Furthermore, if the rewards correspond to a minimum rank embedding, the regret boils down to $O(\chi(\overline{G})\sqrt{T})$—none of the existing works were able to bring out such influences of graph structures over arm rewards. Finally noting that computing the above minimum rank embedding is NP-Hard, we also propose an alternative $O(N + |E|)$ time computable embedding scheme—{\it Greedy Embeddings}—based on greedy graph coloring, on which our algorithms perform optimally on a large family of graphs, e.g. union of cliques, complement of $k$-colorable graphs, regular graphs etc, and are also shown to outperform the state-of-the-art methods on real datasets. Our findings open up new roads for exploiting graph structures on regret performance.