  We formulate a new problem at the intersectionof semi-supervised learning and
contextual bandits,motivated by several applications including clini-cal trials
and ad recommendations. We demonstratehow Graph Convolutional Network (GCN), a
semi-supervised learning approach, can be adjusted tothe new problem
formulation. We also propose avariant of the linear contextual bandit with
semi-supervised missing rewards imputation. We thentake the best of both
approaches to develop multi-GCN embedded contextual bandit. Our algorithmsare
verified on several real world datasets.
