Dual-View Fuzzy Membership and Candidate-Adaptive Multi-Source Evidence Fusion for Hyperedge Prediction

Hyperedge prediction requires identifying missing higher-order relations from incomplete hypergraphs. Existing approaches typically encode candidate hyperedges into unified representations, implicitly combining heterogeneous formation cues and overlooking how different candidates rely on different types of evidence. However, candidate hyperedges may emerge from diverse formation patterns, driven by structural participation, attribute semantics, or their cross-view consistency. Therefore, prediction requires explicitly modeling these complementary formation evidences and adaptively determining their contributions for each candidate. To address this issue, we propose FAME-HP, a framework that models candidate hyperedges through dual-view fuzzy overlapping memberships and candidate-adaptive evidence fusion. FAME-HP learns structural and semantic membership spaces, derives candidate-level structural, semantic, and cross-view consistency evidence, and dynamically adjusts their contributions according to candidate-specific contexts. Experiments on six real-world hypergraphs under four evaluation protocols demonstrate that FAME-HP achieves the best or second-best performance across all six datasets in average AUROC and average precision, outperforming strong hyperedge prediction baselines on most benchmarks. These results demonstrate that explicitly organizing and adapting heterogeneous formation evidence provides an effective approach for identifying missing higher-order relations.