Convergence and Stability of Graph Convolutional Networks on Large Random Graphs

2 Jun 2020Nicolas KerivenAlberto BiettiSamuel Vaiter

We study properties of Graph Convolutional Networks (GCNs) by analyzing their behavior on standard models of random graphs, where nodes are represented by random latent variables and edges are drawn according to a similarity kernel. This allows us to overcome the difficulties of dealing with discrete notions such as isomorphisms on very large graphs, by considering instead more natural geometric aspects... (read more)

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