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Experimental results demonstrate that the coarsening technique significantly accelerates dynamic GNN training and inference without compromising predictive performance, offering a practical path toward scalable dynamic graph learning.
Contrastive Conformal HGNN (CCF-HGNN) is proposed that accounts for uncertainty in hypergraph-based models by explicitly regularizing on the hypergraph structure for guaranteed and robust uncertainty estimates.
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,...
Experimental results demonstrate that the coarsening technique significantly accelerates dynamic GNN training and inference without compromising predictive performance, offering a practical path toward scalable dynamic graph learning.
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