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Broad Learning Based on Subgraph Networks for Graph Classification,rks, etc., where subgraphs or motifs can be considered as network building blocks with particular functions to capture mesoscopic structures. Most existing studies ignored the interaction between these subgraphs, which could be of particular importance to represent the global structure at the subgra
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Subgraph Augmentation with Application to Graph Mining,in analysis etc. However, the limitation of the general scale of benchmark datasets makes easily causes graph classification models to fall into overfitting and undergeneralization. In this chapter, the . framework is introduced for graph classification (Zhou et al., Data augmentation for graph clas
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