Node Classification

324 papers with code • 71 benchmarks • 22 datasets

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 )

Greatest papers with code

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

google-research/google-research KDD 2019

Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99. 36 on the PPI dataset, while the previous best result was 98. 71 by [16].

 Ranked #1 on Node Classification on Pubmed (F1 metric)

Graph Clustering Link Prediction +1

Principal Neighbourhood Aggregation for Graph Nets

rusty1s/pytorch_geometric NeurIPS 2020

Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.

Graph Classification Graph Regression +1

Diffusion Improves Graph Learning

rusty1s/pytorch_geometric NeurIPS 2019

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

rusty1s/pytorch_geometric 9 Apr 2019

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

General Classification Node Classification +1

Fast Graph Representation Learning with PyTorch Geometric

rusty1s/pytorch_geometric 6 Mar 2019

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 +2

Hypergraph Convolution and Hypergraph Attention

rusty1s/pytorch_geometric 23 Jan 2019

To efficiently learn deep embeddings on the high-order graph-structured data, we introduce two end-to-end trainable operators to the family of graph neural networks, i. e., hypergraph convolution and hypergraph attention.

Node Classification Representation Learning

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

rusty1s/pytorch_geometric CVPR 2018

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.

General Classification Graph Classification +2

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

dmlc/dgl 3 Sep 2019

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

Graph Learning Node Classification

Semi-Supervised Classification with Graph Convolutional Networks

tkipf/gcn 9 Sep 2016

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 General Classification +4