Add to ORKGGraph neural networks (GNNs) are composed of layers consisting of graph convolutions and pointwise nonlinearities. Due to their invariance and stability properties, GNNs are provably successful at learning representations from data supported on moderate-scale graphs. However, they are difficult to learn on large-scale graphs. In this paper, we study the problem of training GNNs on graphs of moderate size and transferring them to large-scale graphs. We use graph limits called graphons to define limit objects for graph filters and GNNs -- graphon filters and graphon neural networks (WNNs) -- which we interpret as generative models for graph filters and GNNs. We then show that graphon filters and WNNs can be approximated by graph filters and G...
One of the most predominant techniques that have achieved phenomenal success in many modern applicat...
Although theoretical properties such as expressive power and over-smoothing of graph neural networks...
Seminal works on graph neural networks have primarily targeted semi-supervised node classification p...
Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and pointwise n...
Graph neural networks (GNNs) are successful at learning representations from most types of network d...
Graph neural networks (GNNs) are successful at learning representations from most types of network d...
Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes ...
Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked ...
Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes ...
The theme of this dissertation is machine learning on graph data. Graphs are generic models of signa...
Graph neural networks (GNNs) use graph convolutions to exploit network invariances and learn meaning...
Graph-based deep learning has been successful in various industrial settings and applications. Howev...
Graph convolutional networks (GCNs) can successfully learn the graph signal representation by graph ...
Graph neural networks (GNNs) are a class of machine learning models that relax the independent and ...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
One of the most predominant techniques that have achieved phenomenal success in many modern applicat...
Although theoretical properties such as expressive power and over-smoothing of graph neural networks...
Seminal works on graph neural networks have primarily targeted semi-supervised node classification p...
Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and pointwise n...
Graph neural networks (GNNs) are successful at learning representations from most types of network d...
Graph neural networks (GNNs) are successful at learning representations from most types of network d...
Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes ...
Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked ...
Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes ...
The theme of this dissertation is machine learning on graph data. Graphs are generic models of signa...
Graph neural networks (GNNs) use graph convolutions to exploit network invariances and learn meaning...
Graph-based deep learning has been successful in various industrial settings and applications. Howev...
Graph convolutional networks (GCNs) can successfully learn the graph signal representation by graph ...
Graph neural networks (GNNs) are a class of machine learning models that relax the independent and ...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
One of the most predominant techniques that have achieved phenomenal success in many modern applicat...
Although theoretical properties such as expressive power and over-smoothing of graph neural networks...
Seminal works on graph neural networks have primarily targeted semi-supervised node classification p...