Explainability Methods for Graph Convolutional Neural Networks

CVPR 2019 Phillip E. Pope Soheil Kolouri Mohammad Rostami Charles E. Martin Heiko Hoffmann

With the growing use of graph convolutional neural networks (GCNNs) comes the need for explainability. In this paper, we introduce explainability methods for GCNNs... (read more)

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