Conformal Link Prediction with False Discovery Rate Control
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915379779993600 |
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| author | Du, Wenqin Ma, Wanteng Xia, Dong Zhang, Yuan Zhou, Wen |
| author_facet | Du, Wenqin Ma, Wanteng Xia, Dong Zhang, Yuan Zhou, Wen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07025 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conformal Link Prediction with False Discovery Rate Control Du, Wenqin Ma, Wanteng Xia, Dong Zhang, Yuan Zhou, Wen Methodology Statistics Theory 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. |
| title | Conformal Link Prediction with False Discovery Rate Control |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2507.07025 |