MDL-GBG selects the shortest-description-length model among single-ball, two-ball, and core-ball-plus-residual options to generate stable granular balls that improve downstream clustering on UCI datasets.
Topological structure in visual perception
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A multi-granularity granular-ball coarsening algorithm reduces large graphs in linear time for faster GCN training on node classification, with experiments claiming superior performance over prior methods.
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MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering
MDL-GBG selects the shortest-description-length model among single-ball, two-ball, and core-ball-plus-residual options to generate stable granular balls that improve downstream clustering on UCI datasets.
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Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
A multi-granularity granular-ball coarsening algorithm reduces large graphs in linear time for faster GCN training on node classification, with experiments claiming superior performance over prior methods.