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Most link prediction methods return estimates of the connection probabilityof missing edges in a graph. Such output can be used to rank the missing edges,from most to least likely to be a true edge, but it does not directly provide aclassification into true and non-existent. In this work, we consider theproblem of identifying a set of true edges with a control of the falsediscovery rate (FDR). We propose a novel method based on high-level ideas fromthe literature on conformal inference. The graph structure induces intricatedependence in the data, which we carefully take into account, as this makes thesetup different from the usual setup in conformal inference, whereexchangeability is assumed. The FDR control is empirically demonstrated forboth simulated and real data.

Conformal link prediction to control the error rate