[Submitted on 4 Sep 2026]
Title:Multi-granularity Adaptive Hypergraph Representation Learning via Granular-ball
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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.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2609.05574 [cs.LG]
(or arXiv:2609.05574v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.05574
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xiaoyu Lian [view email] [v1] Fri, 4 Sep 2026 07:46:18 UTC (384 KB)
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