3 papers
cs.LG2026
Multi-granularity Adaptive Hypergraph Representation Learning via Granular-ball
Sen Zhao, Yifan Guan, Jinyuan Ni +6
Hypergraph representation learning aims to capture high-order information in graphs by constructing hyperedges that simultaneously connect multiple nodes. These hyperedges adapt to…
cs.AI2026
Global to Local: Topology-Preserving Adaptive Graph Pooling via Granular-Ball
Sen Zhao, Gaojie Xu, Shuyin Xia +4
Graph pooling aims to compress the graph, including both node embeddings and their underlying topological patterns, into a more compact representation. Previous works focus primari…
cs.AI2025
GBGC: Efficient and Adaptive Graph Coarsening via Granular-ball Computing
Shuyin Xia, Guan Wang, Gaojie Xu +2
The objective of graph coarsening is to generate smaller, more manageable graphs while preserving key information of the original graph. Previous work were mainly based on the pers…