Learning Robust Hypergraph Embeddings for Distribution-Free Uncertainty Quantification

Abstract

Hypergraph representation learning has gained immense popularity over the last few years due to its applications in real-world domains like social network analysis, recommendation systems, biological network modeling, and knowledge graphs. However, hypergraph neural networks (HGNNs) lack rigorous uncertainty estimates, which limits their deployment in critical applications where the reliability of predictions is crucial. To bridge this gap, we propose Contrastive Conformal HGNN (CCF-HGNN) that accounts for uncertainty in hypergraph-based models by explicitly regularizing on the hypergraph structure for guaranteed and robust uncertainty estimates. CCF-HGNN accounts for epistemic uncertainty in HGNN predictions by producing a prediction set that leverages the topological structure and provably contains the true label with a pre-defined coverage probability. It also accounts for aleatoric uncertainty by leveraging contrastive learning on the structure of the hypergraph. To enhance the power of the predictions, CCF-HGNN performs an additional auxiliary task of hyperedge degree prediction with an end-to-end differentiable sampling-based approach. Extensive experiments on real-world hypergraph datasets demonstrate the superiority of CCF-HGNN by improving the efficiency of prediction sets while maintaining valid coverage.