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 Multi-Granularity Hypergraph Representation Learning (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.