PANDA：先验引导的注意力双路径架构
PANDA: Prior-guided Attentional Dual-path Architecture
AI总结：
本文提出PANDA插件，用于改进超图神经网络，通过双路径架构解决注意力竞争性问题，在八个真实世界基准数据集上平均提升5.18个百分点。
摘要：
超图神经网络（HNN）用于建模超越成对交互的高阶关系，通常依赖固定结构权重或归一化注意力。固定权重会在异质环境中传播误导性信息，而归一化会使注意力系数具有竞争性——在消息传递框架中，增加一个发送者的权重必须减少同一聚合集中其他发送者的权重。本文的主要贡献是引入PANDA（Prior-guided AttentioNal Dual-path Architecture，先验引导的注意力双路径架构），这是一种适用于两阶段消息传递HNN的插件，可应用于无向和有向场景。主路径在学习到的接收者归一化注意力与结构关联先验之间进行插值，而非竞争性（未归一化）的辅助路径则让每个发送者贡献自身变换后的信息。我们还证明，使用固定结构系数时，PANDA可恢复先前工作中采用的某一复值卷积算子。在涵盖空间和光谱方法、无向和有向场景的四种HNN上，将PANDA插件接入后，在八个真实世界基准数据集上平均提升5.18个百分点。代码可在该https URL获取。
英文摘要：
Hypergraph Neural Networks (HNNs), designed to model higher-order relations beyond pairwise interactions, typically rely either on fixed structural weights or on normalised attention. Fixed weights can propagate misleading information in heterophilic settings, while normalisation 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 plug-in for two-stage message passing HNNs applicable to both undirected and directed settings. The main path interpolates between learned receiver-normalised attention and structural incidence priors, while a non-competitive (not normalised) 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. The code is available at https://github.com/jainris/panda.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
