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
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
The method generates granular balls in the current fused kernel space and alternates kernel-weight learning with granular-ball membership updates, allowing the representation to adapt to changes in the fused-kernel geometry.
Xiao-Yu Lian, Yu-Chao Zhang, Shu-Yin Xia et al.· 0 citations
Experimental results demonstrate that the proposed adaptive and efficient KNN approach via granular-ball computing outperforms existing KNN variants across multiple datasets in terms of both accuracy and efficiency.
Xiao-Yu Lian, Shu-Yin Xia, Hongxuan He et al.· 0 citations
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