Conformal link prediction for false discovery rate control

TL;DR

This paper introduces a conformal inference-based method for link prediction in graphs that controls the false discovery rate, effectively identifying true edges despite complex data dependencies.

Contribution

It develops a novel conformal inference approach tailored for graph data with dependence, enabling FDR control in link prediction tasks.

Findings

FDR control demonstrated on simulated data

Effective in real-world graph datasets

Addresses dependence in graph-structured data

Abstract

Most link prediction methods return estimates of the connection probability of 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 does not directly provide a classification into true and non-existent. In this work, we consider the problem of identifying a set of true edges with a control of the false discovery rate (FDR). We propose a novel method based on high-level ideas from the literature on conformal inference. The graph structure induces intricate dependence in the data, which we carefully take into account, as this makes the setup different from the usual setup in conformal inference, where data exchangeability is assumed. The FDR control is empirically demonstrated for both simulated and real data.