AD-DHGNN: Adaptive Dual-Controlled Dynamic Hypergraph Neural Networks

Dynamic hypergraph neural networks adapt high-order relations to evolving node representations, yet most existing constructions rely on fixed hyperedge counts or hyperedge cardinality constraints. Such fixed structural choices can produce redundant, oversized, or fragmented hypergraphs when the required relation structure varies across layers and datasets. We propose the Adaptive Dual-Controlled Dynamic Hypergraph Neural Network AD-DHGNN, which treats global hyperedge count and local hyperedge cardinality as two complementary structural variables. AD-DHGNN first generates posterior-weighted candidate hyperedges from anchor-based node similarities, avoiding dense node-pair affinity estimation. It then applies a cardinality controller that assigns data-dependent capacity budgets to individual candidates, followed by a count controller that selects a compact and representative subset of hyperedges. The resulting sparse hypergraph is used for gated dynamic message passing. Experiments on seven node-classification benchmarks show that AD-DHGNN improves predictive performance over the evaluated baselines while constructing compact and low-redundancy dynamic hypergraphs. These results demonstrate that explicitly determining hyperedge count and hyperedge cardinality is a central principle for scalable dynamic hypergraph reconstruction.