# Conformal Network Link Prediction with False Discovery Rate Control under Unstructured Missingness

arXiv: https://arxiv.org/abs/2507.07025 · HTML: https://mlsanity.com/paper/2507.07025/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [Conformal Prediction for Dyadic Regression Under Complex Missingness](https://mlsanity.com/paper/2606.11136/) (2026)

## Abstract (arXiv)

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 missing patterns. We introduce conformal link prediction, 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.