Graph Key Feature Extraction based on Bag of Features Model and Adjacent Point Pattern

抄録

For graph classification tasks, graph kernels based on the R-convolution framework are effective tools which aims to decompose graphs into substructures. However, the current R-convolution framework has a weak point that its aggregating strategy of substructure similarities is too simple, which is based on unweighted summation and multiplication of substructure similarities. This means that it may have less robustness. In our works, we tend to combine the Bag of Feature (BoF) model and the Adjacent Point Pattern to form a more effective framework for graph key feature extraction, which also supports large datasets.