提出新算法DOSAGE，高效构建密集重叠子图以增强超图神经网络性能。
Hyperedge Modeling in Hypergraph Neural Networks by using Densest Overlapping Subgraphs
- 用凝聚式贪心枚举法寻找密集重叠子图，提升超图构建质量。
- 在节点分类任务中显著优于HGNN及六种基准方法。
- 适合研究复杂关系建模与超图结构优化的学者使用。
超图通过引入超边突破传统图的局限，超边可连接任意数量节点，其消息传递机制为顶点-超边-顶点形式，能捕捉比传统图神经网络更丰富复杂的结构信息。近年来，重叠子图概念兴起，允许节点属于多个子图，从而更准确地表达子群体间的关系。其中，寻找密集重叠子图（DOS）是图聚类中的核心问题。本文提出一种新算法DOSAGE（Agglomerative Greedy Enumeration），用于高效生成密集重叠子图，进而实现鲁棒的超图构造。在标准数据集上的实验表明，该方法在节点分类任务上显著优于传统HGNN及其他六种方法。
原文摘要 · Abstract (English)
Hypergraphs tackle the limitations of traditional graphs by introducing {\em hyperedges}. While graph edges connect only two nodes, hyperedges connect an arbitrary number of nodes along their edges. Also, the underlying message-passing mechanisms in Hypergraph Neural Networks (HGNNs) are in the form of vertex-hyperedge-vertex, which let HGNNs capture and utilize richer and more complex structural information than traditional Graph Neural Networks (GNNs). More recently, the idea of overlapping subgraphs has emerged. These subgraphs can capture more information about subgroups of vertices without limiting one vertex belonging to just one group, allowing vertices to belong to multiple groups or subgraphs. In addition, one of the most important problems in graph clustering is to find densest overlapping subgraphs (DOS). In this paper, we propose a solution to the DOS problem via Agglomerative Greedy Enumeration (DOSAGE) algorithm as a novel approach to enhance the process of generating the densest overlapping subgraphs and, hence, a robust construction of the hypergraphs. Experiments on standard benchmarks show that the DOSAGE algorithm significantly outperforms the HGNNs and six other methods on the node classification task.
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