# Hyperedge Modeling in Hypergraph Neural Networks by using Densest Overlapping Subgraphs

arXiv: https://arxiv.org/abs/2409.10340 · HTML: https://mlsanity.com/paper/2409.10340/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting](https://mlsanity.com/paper/2006.09252/) (2020)
- [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019)
- [Hypergraph Co-Optimal Transport: Metric and Categorical Properties](https://mlsanity.com/paper/2112.03904/) (2021)
- [Feature Selection and Extraction for Graph Neural Networks](https://mlsanity.com/paper/1910.10682/) (2019)
- [The Generalized Mean Densest Subgraph Problem](https://mlsanity.com/paper/2106.00909/) (2021)
- [Evolution of Cooperation in the Presence of Higher-Order Interactions: from Networks to Hypergraphs](https://mlsanity.com/paper/2006.03893/) (2020)

## Built on by (most cited first, 1 of 1)

- [Hypergraph Neural Networks Reveal Spatial Domains from Single-cell Transcriptomics Data](https://mlsanity.com/paper/2410.19868/) (2024)

## Abstract (arXiv)

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.