Wide and deep graph neural network with distributed online learning
Graph neural networks (GNNs) are naturally distributed architectures for learning
representations from network data. This renders them suitable candidates for decentralized …
representations from network data. This renders them suitable candidates for decentralized …
Learning stable graph neural networks via spectral regularization
Stability of graph neural networks (GNNs) characterizes how GNNs react to graph
perturbations and provides guarantees for architecture performance in noisy scenarios. This …
perturbations and provides guarantees for architecture performance in noisy scenarios. This …
Learning stochastic graph neural networks with constrained variance
Stochastic graph neural networks (SGNNs) are information processing architectures that
learn representations from data over random graphs. SGNNs are trained with respect to the …
learn representations from data over random graphs. SGNNs are trained with respect to the …