A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings

2022/12/28 by Ryan Aponte, Aponte, Ryan, Ryan A. Rossi +15 · 1 citation
Computer Science · #Advanced Graph Neural Networks #Data Visualization and Analytics #Discrete Mathematics (cs.DM) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Social and Information Networks (cs.SI)

paper · pdf · doi:10.48550/arxiv.2212.14077

openalex publication_date 2022/12/28 · openalex created_date 2023/01/06 · openalex updated_date 2026/07/28

Abstract

In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.

Cited by

• Identifying Group Anchors in Real-World Group Interactions Under Label Scarcity

Related

• Hypergraph Pre-training with Graph Neural Networks
• Hyperedge Interaction-aware Hypergraph Neural Network
• Hypergraph Neural Networks
• HyperKAN: Hypergraph Representation Learning with Kolmogorov-Arnold Networks
• Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks