Towards Stable, Globally Expressive Graph Representations with Laplacian
Eigenvectors
Towards Stable, Globally Expressive Graph Representations with Laplacian
Eigenvectors
Graph neural networks (GNNs) have achieved remarkable success in a variety of machine learning tasks over graph data. Existing GNNs usually rely on message passing, i.e., computing node representations by gathering information from the neighborhood, to build their underlying computational graphs. They are known fairly limited in expressive power, and …