AutoHGNN：面向超图神经网络的鲁棒且高效的神经架构搜索
AutoHGNN: Robust and Efficient Neural Architecture Search for Hypergraph Neural Networks
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中文总结 AI 辅助
本文提出AutoHGNN，一种针对超图神经网络的神经架构搜索框架，通过引入超交互模块和超图稳定拓扑距离，在多种基准上实现更优的分类准确率与时间效率。
中文摘要 AI 辅助
近年来，超图神经网络取得了显著的成功。然而，手动设计架构既耗费人力，又往往难以捕捉复杂的高阶关系，因此实现超图神经网络结构设计的自动化至关重要。为提高超图学习的自动化程度和适应性，本文提出了AutoHGNN，一个专为超图神经网络量身定制的神经架构搜索框架。首先，我们在搜索空间中引入超交互模块（HIM），以解决传统图神经网络设计与超图数据之间的不匹配问题。其次，我们提出超图稳定拓扑距离（HyperSTD）作为结构选择标准，以在可微搜索过程中识别最能保留原始超图内在结构亲和力的架构。在多种基准数据集上进行的大量实验表明，AutoHGNN在分类准确性和时间效率上始终优于手动设计和自动搜索的基线方法，证明所发现的架构显著更加有效。
英文摘要
Hypergraph neural networks have achieved significant success in recent years. However, manual architecture crafting is labor-intensive and often fails to capture complex, higher-order relations, making the automation of hypergraph neural network structure design crucial. To improve the automation and adaptability of hypergraph learning, this paper proposes AutoHGNN, a neural architecture search framework tailored for hypergraph neural networks. First, we introduce a Hyper-Interaction Module (HIM) into the search space to address the mismatch between conventional graph neural network designs and hypergraph data. Second, we propose Hypergraph Stable Topological Distance (HyperSTD) as a structural selection criterion to identify architectures that best preserve the intrinsic structural affinities of the original hypergraph during differentiable search. Extensive experiments on various benchmark datasets demonstrate that AutoHGNN consistently outperforms manually designed and automatically searched baselines in classification accuracy and time efficiency, proving that the discovered architectures are significantly more effective.
