14:00 〜 14:20
[2N3-IS-2b-03] Graph Key Feature Extraction based on Bag of Features Model and Adjacent Point Pattern
キーワード：Graph matching, Graph classification, Clutering
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.
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.
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