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Found 4 papers, 0 papers with code
Asymptotic generalization error of a single-layer graph convolutional network
While graph convolutional networks show great practical promises, the theoretical understanding of their generalization properties as a function of the number of samples is still in its infancy compared to the more broadly studied case of supervised fully connected neural networks.
Optimal Inference in Contextual Stochastic Block Models
We show that there can be a considerable gap between the accuracy reached by this algorithm and the performance of the GNN architectures proposed in the literature.
Neural-prior stochastic block model
The stochastic block model (SBM) is widely studied as a benchmark for graph clustering aka community detection.
Modelling the influence of data structure on learning in neural networks
Our analysis reveals two phenomena related to the dynamics of the networks and their ability to generalise that only appear when training on structured data sets.
