Transferability of Graph Neural Networks: an Extended Graphon Approach

Understand

We study spectral graph convolutional neural networks (GCNNs), where filters are defined as continuous functions of the graph shift operator (GSO) through functional calculus.

• A spectral GCNN is not tailored to one specific graph and can be transferred between different graphs.
• It is hence important to study the GCNN transferability: the capacity of the network to have approximately the same repercussion on different graphs that represent the same phenomenon.
• Transferability ensures that GCNNs trained on certain graphs generalize if the graphs in the test set represent the same phenomena as the graphs in the training set.