Diffusion improves graph learning
J Gasteiger, S Weißenberger… - Advances in neural …, 2019 - proceedings.neurips.cc
Graph convolution is the core of most Graph Neural Networks (GNNs) and usually
approximated by message passing between direct (one-hop) neighbors. In this work, we …
approximated by message passing between direct (one-hop) neighbors. In this work, we …
Diffusion-convolutional neural networks
J Atwood, D Towsley - Advances in neural information …, 2016 - proceedings.neurips.cc
We present diffusion-convolutional neural networks (DCNNs), a new model for graph-
structured data. Through the introduction of a diffusion-convolution operation, we show how …
structured data. Through the introduction of a diffusion-convolution operation, we show how …
Simple spectral graph convolution
Graph Convolutional Networks (GCNs) are leading methods for learning graph
representations. However, without specially designed architectures, the performance of …
representations. However, without specially designed architectures, the performance of …
Dual graph convolutional networks for graph-based semi-supervised classification
The problem of extracting meaningful data through graph analysis spans a range of different
fields, such as the internet, social networks, biological networks, and many others. The …
fields, such as the internet, social networks, biological networks, and many others. The …