# Line Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs

arXiv: https://arxiv.org/abs/2002.03392 · HTML: https://mlsanity.com/paper/2002.03392/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2020.

## Built on by (most cited first, 11 of 11)

- [Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond](https://mlsanity.com/paper/2101.05510/) (2021)
- [Graph Neural Networks Designed for Different Graph Types: A Survey](https://mlsanity.com/paper/2204.03080/) (2022)
- [TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks](https://mlsanity.com/paper/2410.06530/) (2024)
- [A Survey on Hyperlink Prediction](https://mlsanity.com/paper/2207.02911/) (2022)
- [Recent Advances in Hypergraph Neural Networks](https://mlsanity.com/paper/2503.07959/) (2025)
- [Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance](https://mlsanity.com/paper/2410.16935/) (2024)
- [Hypernetwork Dismantling via Deep Reinforcement Learning](https://mlsanity.com/paper/2104.14332/) (2021)
- [The CAT SET on the MAT: Cross Attention for Set Matching in Bipartite Hypergraphs](https://mlsanity.com/paper/2111.00243/) (2021)
- [Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations](https://mlsanity.com/paper/2211.01174/) (2022)
- [Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property](https://mlsanity.com/paper/2210.05947/) (2022)
- [Hypergraph Echo State Network](https://mlsanity.com/paper/2310.10177/) (2023)

## Abstract (arXiv)

Network representation learning and node classification in graphs got significant attention due to the invent of different types graph neural networks. Graph convolution network (GCN) is a popular semi-supervised technique which aggregates attributes within the neighborhood of each node. Conventional GCNs can be applied to simple graphs where each edge connects only two nodes. But many modern days applications need to model high order relationships in a graph. Hypergraphs are effective data types to handle such complex relationships. In this paper, we propose a novel technique to apply graph convolution on hypergraphs with variable hyperedge sizes. We use the classical concept of line graph of a hypergraph for the first time in the hypergraph learning literature. Then we propose to use graph convolution on the line graph of a hypergraph. Experimental analysis on multiple real world network datasets shows the merit of our approach compared to state-of-the-arts.