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Node Classification

170 papers with code · Graphs

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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Greatest papers with code

Diffusion Improves Graph Learning

NeurIPS 2019 rusty1s/pytorch_geometric

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).

GRAPH LEARNING NODE CLASSIFICATION

Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks

9 Apr 2019rusty1s/pytorch_geometric

We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs.

NODE CLASSIFICATION REPRESENTATION LEARNING

Fast Graph Representation Learning with PyTorch Geometric

6 Mar 2019rusty1s/pytorch_geometric

We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

CVPR 2018 rusty1s/pytorch_geometric

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.

GRAPH CLASSIFICATION NODE CLASSIFICATION

Benchmarking Graph Neural Networks

2 Mar 2020dmlc/dgl

Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

GRAPH CLASSIFICATION GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION

Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

3 Sep 2019dmlc/dgl

Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.

GRAPH LEARNING NODE CLASSIFICATION

Graph Attention Networks

ICLR 2018 aymericdamien/TopDeepLearning

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.

DOCUMENT CLASSIFICATION GRAPH EMBEDDING GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

Semi-Supervised Classification with Graph Convolutional Networks

9 Sep 2016tkipf/gcn

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.

DOCUMENT CLASSIFICATION GRAPH CLASSIFICATION GRAPH REGRESSION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

NeurIPS 2016 tkipf/gcn

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.

NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION