BHyGNN+: 无监督学习异质超图的表示学习
BHyGNN+: Unsupervised Representation Learning for Heterophilic Hypergraphs
AI总结：
BHyGNN+通过超图对偶性实现无监督学习，有效提升异质超图表示学习性能。
摘要：
超图神经网络（HyGNNs）在建模实体之间的高阶关系方面表现出色。然而，它们在异质超图上的性能往往会下降，因为在同一条超边连接的节点倾向于具有不同的语义表示或属于不同的类别。尽管已经提出了几种HyGNN，包括我们之前的工作BHyGNN，以解决异质性问题，但它们对标注数据的依赖性显著限制了其在现实世界中的应用，因为在现实世界中标注数据稀缺或成本高昂。为克服这一限制，我们引入了BHyGNN+，一种自监督学习框架，它扩展了BHyGNN以在异质超图上进行表示学习，而无需真实标签。BHyGNN+的核心思想是超图对偶性，一种结构转换，其中节点和超边的角色相互转换。通过使用余弦相似度对比超图的增强视图与其对偶体，我们的框架以完全无监督的方式捕捉了本质的结构模式。值得注意的是，这种基于对偶性的公式消除了对负样本的需要，这是现有超图对比学习方法中常见的要求，但通常在实践中难以满足。在 eleven 个基准数据集上的广泛实验表明，BHyGNN+在异质和同质超图上都优于最先进的监督和自监督基线。我们的结果验证了利用超图对偶性进行自监督学习的有效性，并在挑战性、无标签的超图上建立了新的表示学习范式。
英文摘要：
Hypergraph Neural Networks (HyGNNs) have demonstrated remarkable success in modeling higher-order relationships among entities. However, their performance often degrades on heterophilic hypergraphs, where nodes connected by the same hyperedge tend to have dissimilar semantic representations or belong to different classes. While several HyGNNs, including our prior work BHyGNN, have been proposed to address heterophily, their reliance on labeled data significantly limits their applicability in real-world scenarios where annotations are scarce or costly. To overcome this limitation, we introduce BHyGNN+, a self-supervised learning framework that extends BHyGNN for representation learning on heterophilic hypergraphs without requiring ground-truth labels. The core idea of BHyGNN+ is hypergraph duality, a structural transformation where the roles of nodes and hyperedges are interchanged. By contrasting augmented views of a hypergraph against its dual using cosine similarity, our framework captures essential structural patterns in a fully unsupervised manner. Notably, this duality-based formulation eliminates the need for negative samples, a common requirement in existing hypergraph contrastive learning methods that is often difficult to satisfy in practice. Extensive experiments on eleven benchmark datasets demonstrate that BHyGNN+ consistently outperforms state-of-the-art supervised and self-supervised baselines on both heterophilic and homophilic hypergraphs. Our results validate the effectiveness of leveraging hypergraph duality for self-supervised learning and establish a new paradigm for representation learning on challenging, unlabeled hypergraphs.
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。
