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Link prediction is one of the fundamental problems in social network analysis. A common set of techniques for link prediction rely on similarity metrics which use the topology of the observed subnetwork to quantify the likelihood of unobserved links. Recently, similarity metrics for link prediction have been shown to be vulnerable to attacks whereby observations about the network are adversarially modified to hide target links. We propose a novel approach for increasing robustness of similarity-based link prediction by endowing the analyst with a restricted set of reliable queries which accurately measure the existence of queried links. The analyst aims to robustly predict a collection of possible links by optimally allocating the reliable queries. We formalize the analyst's problem as a Bayesian Stackelberg game in which they first choose the reliable queries, followed by an adversary who deletes a subset of links among …