Multi-granularity Adaptive Hypergraph Representation Learning via Granular-ball

• URL: http://arxiv.org/abs/2609.05574v1
• Date: Fri, 04 Sep 2026 07:46:18 GMT
• Title: Multi-granularity Adaptive Hypergraph Representation Learning via Granular-ball
• Abstract summary: Hypergraph representation learning aims to capture high-order information in graphs by constructing hyperedges that simultaneously connect multiple nodes.<n>We introduce an Adaptive Granular Hypergraph Generation strategy, which generates hyperedges at multiple levels of granularity.<n>We also propose a Multi-Granularity Hypergraph Network with multiple sub-networks, capturing features from hyperedges at different granularities.
• Score: 7.889791816768536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hypergraph representation learning aims to capture high-order information in graphs by constructing hyperedges that simultaneously connect multiple nodes. These hyperedges adapt to the graph's topological features, facilitating the extraction of high-order relationships at multiple granularities. Most prior work relies on predefined definitions to generate hyperedges, overlooking the diversity in graph topological structures and the multi-granularity characteristics of hyperedges. As a result, this limits their ability to effectively and adaptively discover high-order relationships and efficiently process complex structural information. To address this limitation, we propose a novel framework called \underline{M}ulti-\underline{G}ranularity \underline{H}ypergraph \underline{R}epresentation \underline{L}earning (MGHRL). MGHRL introduces an Adaptive Granular Hypergraph Generation strategy, which generates hyperedges at multiple levels of granularity through the adaptive splitting of granular-ball, effectively capturing high-order relationships based on the graph's topological structure. Additionally, we propose a Multi-Granularity Hypergraph Network with multiple sub-networks, capturing features from hyperedges at different granularities and integrating them via hierarchical reversible connections. Experimental results show that MGHRL significantly outperforms baseline models on benchmark datasets.

Related papers

• Community and hyperedge inference in multiple hypergraphs [9.782518418521175]
  We show how to utilize interconnections between multiple hypergraphs to synthesize integrated information from multiple higher-order systems.<n>We propose a model based on the block model, which integrates information from multiple hypergraphs to reveal latent high-order structures.<n>Our work provides a practical and flexible tool for analyzing multiple hypergraphs, greatly advancing the understanding of the organization in real-world high-order systems.
  arXiv  Detail & Related papers  (2025-05-08T05:52:41Z)
• ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion [73.85920403511706]
  We propose ScaleGNN, a novel framework that adaptively fuses multi-hop node features for scalable and effective graph learning.<n>We show that ScaleGNN consistently outperforms state-of-the-art GNNs in both predictive accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2025-04-22T14:05:11Z)
• Hypergraph Foundation Model [31.16656352968761]
  Hypergraph neural networks (HGNNs) effectively model complex high-order relationships in domains like protein interactions and social networks.<n>We present Hyper-FM, a Hypergraph Foundation Model for multi-domain knowledge extraction.<n>We also curate 10 text-attributed hypergraph datasets to advance research between HGNNs and LLMs.
  arXiv  Detail & Related papers  (2025-03-03T05:56:08Z)
• 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)
• Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design [6.993718963663415]
  We introduce a novel conceptualization of homophily in higher-order networks based on a Message Passing scheme.<n>We investigate some natural, yet mostly unexplored, strategies for processing higher-order structures within HNNs.
  arXiv  Detail & Related papers  (2023-10-11T17:35:20Z)
• 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)
• ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network [72.16255675586089]
  We propose an Adaptive Curvature Exploration Hyperbolic Graph NeuralNetwork named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks. Experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability.
  arXiv  Detail & Related papers  (2021-10-15T07:18:57Z)
• Learnable Hypergraph Laplacian for Hypergraph Learning [34.28748027233654]
  HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph structured data. We propose the first learning-based method tailored for constructing adaptive hypergraph structure, termed HypERgrAph Laplacian aDaptor (HERALD) HERALD adaptively optimize the adjacency relationship between hypernodes and hyperedges in an end-to-end manner and thus the task-aware hypergraph is learned.
  arXiv  Detail & Related papers  (2021-06-12T02:07:07Z)
• Learnable Hypergraph Laplacian for Hypergraph Learning [34.28748027233654]
  HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph structured data. We propose the first learning-based method tailored for constructing adaptive hypergraph structure, termed HypERgrAph Laplacian aDaptor (HERALD) HERALD adaptively optimize the adjacency relationship between hypernodes and hyperedges in an end-to-end manner and thus the task-aware hypergraph is learned.
  arXiv  Detail & Related papers  (2021-06-10T12:37:55Z)
• Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning [88.60285937702304]
  This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE) Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
  arXiv  Detail & Related papers  (2021-03-25T09:31:41Z)

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.