{"ID":2896685,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.07025","arxiv_id":"2507.07025","title":"Conformal Link Prediction with False Discovery Rate Control","abstract":"We propose a new method for predicting multiple missing links in partially observed networks while controlling the false discovery rate (FDR), a largely unresolved challenge in network analysis. The main difficulty lies in handling complex dependencies and unknown, heterogeneous missing patterns. We introduce conformal link prediction ({\\tt clp}), a distribution-free procedure grounded in the exchangeability structure of weighted graphon models. Our approach constructs conformal p-values via a novel multi-splitting strategy that restores exchangeability within local test sets, thereby ensuring valid row-wise FDR control, even under unknown missing mechanisms. To achieve FDR control across all missing links, we further develop a new aggregation scheme based on e-values, which accommodates arbitrary dependence across network predictions. Our method requires no assumptions on the missing rates, applies to weighted, unweighted, undirected, and bipartite networks, and enjoys finite-sample theoretical guarantees. Extensive simulations and real-world data study confirm the effectiveness and robustness of the proposed approach.","short_abstract":"We propose a new method for predicting multiple missing links in partially observed networks while controlling the false discovery rate (FDR), a largely unresolved challenge in network analysis. The main difficulty lies in handling complex dependencies and unknown, heterogeneous missing patterns. We introduce conformal...","url_abs":"https://arxiv.org/abs/2507.07025","url_pdf":"https://arxiv.org/pdf/2507.07025v1","authors":"[\"Wenqin Du\",\"Wanteng Ma\",\"Dong Xia\",\"Yuan Zhang\",\"Wen Zhou\"]","published":"2025-07-09T16:54:40Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"math.ST\"]","methods":"[]","has_code":false}