Author

Yi Wang

3 papers indexed here

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Aug 2026

Multi-Granularity Graph Contrastive Learning Framework via Granular-Ball on Heterogeneous Graphs.

Graph Contrastive Learning (GCL) is a popular self-supervised learning (SSL) technique. However, mainstream GCL methods usually favor single fine-grained random augmentation schemes, which will destroy the structural integrity of the graph, and they largely ignore the topology of the graph structure, that is, multi-gra...

Shuyin Xia, Guan Wang, Cheng Tan et al. · IEEE Transactions on Pattern... · 0 citations

#machine learning Preprint Sep 2026

Multi-granularity Adaptive Hypergraph Representation Learning via Granular-ball

A novel framework called Adaptive Granular Hypergraph Generation (MGHRL) is introduced, 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.

Sen Zhao, Yi-Fan Guan, Jin-Yuan Ni et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Global to Local: Topology-Preserving Adaptive Graph Pooling via Granular-Ball

Graph pooling aims to compress the graph, including both node embeddings and their underlying topological patterns, into a more compact representation. Previous works focus primarily on the overly fine-grained representation of nodes, progressively coarsening the graph by removing nodes or merging them into clusters, t...

Sen Zhao, Gao-Jie Xu, Shuyin Xia et al. · 0 citations