The node classification task is one where the algorithm has to determine the labelling of samples (represented as nodes) by looking at the labels of their neighbours.
( Image credit: Fast Graph Representation Learning With PyTorch Geometric )
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In this work, we remove the restriction of using only the direct neighbors by introducing a powerful, yet spatially localized graph convolution: Graph diffusion convolution (GDC).
Ranked #1 on Node Classification on AMZ Photo
We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs.
Ranked #10 on Node Classification on Citeseer
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
Ranked #2 on Graph Classification on REDDIT-B
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.
Ranked #2 on Node Classification on Cora
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
Ranked #17 on Node Classification on Cora
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Ranked #2 on Node Classification on Wiki-Vote
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
Ranked #1 on Graph Classification on IPC-grounded
In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs.
Ranked #4 on Skeleton Based Action Recognition on SBU