PANDA: Prior-guided Attentional Dual-path Architecture
PANDA introduces a dual-path architecture for Hypergraph Neural Networks that merges data-driven attention with structural knowledge. It uses a main path to interpolate between learned attention and structural priors, while an auxiliary path provides independent, noncompetitive information. This allows the model to dynamically switch between relying on learned data signals and reliable graph structure for better performance.