Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

NeurIPS 2016 Michaël DefferrardXavier BressonPierre Vandergheynst

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs... (read more)

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Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Node Classification Citeseer ChebNet Accuracy 69.8% # 6
Node Classification Cora ChebNet Accuracy 81.2% # 5
Node Classification Pubmed ChebNet Accuracy 74.4% # 7