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

• URL: http://arxiv.org/abs/2212.14077v1
• Date: Wed, 28 Dec 2022 19:45:38 GMT
• Title: A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings
• Authors: Ryan Aponte, Ryan A. Rossi, Shunan Guo, Jane Hoffswell, Nedim Lipka, Chang Xiao, Gromit Chan, Eunyee Koh, Nesreen Ahmed
• Abstract summary: We introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) HNN jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. 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.
• Score: 39.9678554461845
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Hypergraph Transformer for Semi-Supervised Classification [50.92027313775934]
  We propose a novel hypergraph learning framework, HyperGraph Transformer (HyperGT) HyperGT uses a Transformer-based neural network architecture to effectively consider global correlations among all nodes and hyperedges. It achieves comprehensive hypergraph representation learning by effectively incorporating global interactions while preserving local connectivity patterns.
  arXiv  Detail & Related papers  (2023-12-18T17:50:52Z)
• From Hypergraph Energy Functions to Hypergraph Neural Networks [94.88564151540459]
  We present an expressive family of parameterized, hypergraph-regularized energy functions. We then demonstrate how minimizers of these energies effectively serve as node embeddings. We draw parallels between the proposed bilevel hypergraph optimization, and existing GNN architectures in common use.
  arXiv  Detail & Related papers  (2023-06-16T04:40:59Z)
• Classification of Edge-dependent Labels of Nodes in Hypergraphs [17.454063924648896]
  We introduce a classification of edge-dependent node labels as a new problem. This problem can be used as a benchmark task for hypergraph neural networks. We propose WHATsNet, a novel hypergraph neural network that represents the same node differently depending on the hyperedges it participates in.
  arXiv  Detail & Related papers  (2023-06-05T16:50:34Z)
• Equivariant Hypergraph Diffusion Neural Operators [81.32770440890303]
  Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide a promising way to model higher-order relations in data. This work proposes a new HNN architecture named ED-HNN, which provably represents any continuous equivariant hypergraph diffusion operators. We evaluate ED-HNN for node classification on nine real-world hypergraph datasets.
  arXiv  Detail & Related papers  (2022-07-14T06:17:00Z)
• Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs [59.71134113268709]
  We present General Hypergraph Spectral Convolution(GHSC), a general learning framework that can handle EDVW and EIVW hypergraphs. In this paper, we show that the proposed framework can achieve state-of-the-art performance. Experiments from various domains including social network analysis, visual objective classification, protein learning demonstrate that the proposed framework can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-31T10:46:47Z)
• UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks [8.777765815864367]
  Hypergraph, an expressive structure with flexibility to model the higher-order correlations among entities, has recently attracted increasing attention from various research domains. We propose UniGNN, a unified framework for interpreting the message passing process in graph and hypergraph neural networks. Experiments have been conducted to demonstrate the effectiveness of UniGNN on multiple real-world datasets.
  arXiv  Detail & Related papers  (2021-05-03T15:48:34Z)
• HNHN: Hypergraph Networks with Hyperedge Neurons [90.15253035487314]
  HNHN is a hypergraph convolution network with nonlinear activation functions applied to both hypernodes and hyperedges. We demonstrate improved performance of HNHN in both classification accuracy and speed on real world datasets when compared to state of the art methods.
  arXiv  Detail & Related papers  (2020-06-22T14:08:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.