Hypergraph-MLP: Learning on Hypergraphs without Message Passing

• URL: http://arxiv.org/abs/2312.09778v3
• Date: Sun, 2 Jun 2024 22:15:18 GMT
• Title: Hypergraph-MLP: Learning on Hypergraphs without Message Passing
• Authors: Bohan Tang, Siheng Chen, Xiaowen Dong,
• Abstract summary: Many hypergraph neural networks leverage message passing over hypergraph structures to enhance node representation learning. We propose an alternative approach where we integrate the information about hypergraph structures into training supervision without explicit message passing. Specifically, we introduce Hypergraph-MLP, a novel learning framework for hypergraph-structured data.
• Score: 41.43504601820411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hypergraphs are vital in modelling data with higher-order relations containing more than two entities, gaining prominence in machine learning and signal processing. Many hypergraph neural networks leverage message passing over hypergraph structures to enhance node representation learning, yielding impressive performances in tasks like hypergraph node classification. However, these message-passing-based models face several challenges, including oversmoothing as well as high latency and sensitivity to structural perturbations at inference time. To tackle those challenges, we propose an alternative approach where we integrate the information about hypergraph structures into training supervision without explicit message passing, thus also removing the reliance on it at inference. Specifically, we introduce Hypergraph-MLP, a novel learning framework for hypergraph-structured data, where the learning model is a straightforward multilayer perceptron (MLP) supervised by a loss function based on a notion of signal smoothness on hypergraphs. Experiments on hypergraph node classification tasks demonstrate that Hypergraph-MLP achieves competitive performance compared to existing baselines, and is considerably faster and more robust against structural perturbations at inference.

Related papers

• HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs [16.07396492960869]
  We propose a new architecture, HyperBERT, which simultaneously models hypergraph relational structure and text-attributed hypergraphs. Results show that HyperBERT achieves a new state-of-the-art on five challenging text-attributed hypergraph node classification benchmarks.
  arXiv  Detail & Related papers  (2024-02-11T21:16:26Z)
• 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)
• Tensorized Hypergraph Neural Networks [69.65385474777031]
  We propose a novel adjacency-tensor-based textbfTensorized textbfHypergraph textbfNeural textbfNetwork (THNN) THNN is faithful hypergraph modeling framework through high-order outer product feature passing message. Results from experiments on two widely used hypergraph datasets for 3-D visual object classification show the model's promising performance.
  arXiv  Detail & Related papers  (2023-06-05T03:26:06Z)
• Deep Hypergraph Structure Learning [34.972686247703024]
  Learning on high-order correlation has shown superiority in data representation learning, where hypergraph has been widely used in recent decades. How to generate the hypergraph structure among data is still a challenging task. DeepHGSL is designed to optimize the hypergraph structure for hypergraph-based representation learning.
  arXiv  Detail & Related papers  (2022-08-26T10:00:11Z)
• 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)
• Adaptive Neural Message Passing for Inductive Learning on Hypergraphs [21.606287447052757]
  We present HyperMSG, a novel hypergraph learning framework. It adapts to the data and task by learning an attention weight associated with each node's degree centrality. It is robust and outperforms state-of-the-art hypergraph learning methods on a wide range of tasks and datasets.
  arXiv  Detail & Related papers  (2021-09-22T12:24:02Z)
• HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs [24.737560790401314]
  We present HyperSAGE, a novel hypergraph learning framework that uses a two-level neural message passing strategy to accurately and efficiently propagate information through hypergraphs. We show that HyperSAGE outperforms state-of-the-art hypergraph learning methods on representative benchmark datasets.
  arXiv  Detail & Related papers  (2020-10-09T13:28:06Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)

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.