PANDA: Prior-guided Attentional Dual-path Architecture

Hypergraph Neural Networks (HNNs), designed to model higher-order relations beyond pairwise interactions, typically rely either on fixed structural weights or on normalized attention. Fixed weights can propagate misleading information in heterophilic settings, while normalization makes attention coefficients competitive, in the sense that, in a message-passing framework, increasing the weight of one sender must reduce the weight of others in the same aggregation set. Our main contribution is the introduction of PANDA (Prior-guided AttentioNal Dual-path Architecture), a plugin for two-stage message passing HNNs applicable to both undirected and directed settings. The *main path* interpolates between learned receiver-normalized attention and structural incidence priors, while a non-competitive (not normalized) *auxiliary path* lets each sender contribute its own transformed information. We also show that, with fixed structural coefficients, PANDA recovers a certain complex-valued, convolution operator adopted in previous works. Across four HNNs spanning spatial and spectral methods in both directed and undirected settings, plugging PANDA in yields an average improvement of $5.18$ percentage points across eight real-world benchmark datasets.