Node Classification

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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 )

Benchmarks

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Dataset	Best Model	Paper Title	Compare
Cora	SSP	Optimization of Graph Neural Networks with Natural Gradient Descent	See all
Citeseer	SSP	Optimization of Graph Neural Networks with Natural Gradient Descent	See all
Pubmed	NodeNet	NodeNet: A Graph Regularised Neural Network for Node Classification	See all
CiteSeer with Public Split: fixed 20 nodes per class	GRAND	Graph Random Neural Network for Semi-Supervised Learning on Graphs	See all
PubMed with Public Split: fixed 20 nodes per class	K2SL-GCN	Self-supervised Smoothing Graph Neural Networks	See all
Cora with Public Split: fixed 20 nodes per class	GCNII	Simple and Deep Graph Convolutional Networks	See all
PPI	GCNII*	Simple and Deep Graph Convolutional Networks	See all
Cora (0.5%)	CPF-ind_APPNP	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework	See all
Cora (1%)	CPF-ind-APPNP	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework	See all
Cora (3%)	CPF-tra-GCNII	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework	See all
CiteSeer (0.5%)	MT-GCN	Mutual Teaching for Graph Convolutional Networks	See all
CiteSeer (1%)	Truncated Krylov	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks	See all
PubMed (0.03%)	Truncated Krylov	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks	See all
PubMed (0.05%)	Truncated Krylov	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks	See all
PubMed (0.1%)	Truncated Krylov	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks	See all
Coauthor CS	CoLinkDist	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	See all
Reddit	JKNet+DropEdge	DropEdge: Towards Deep Graph Convolutional Networks on Node Classification	See all
PATTERN 100k	DGN	Directional Graph Networks	See all
BlogCatalog	DEMO-Net(weight)	DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification	See all
Chameleon	FSGNN	Improving Graph Neural Networks with Simple Architecture Design	See all
Squirrel	HLP Concat	Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs	See all
Actor	FSGNN	Improving Graph Neural Networks with Simple Architecture Design	See all
Cornell	FSGNN	Improving Graph Neural Networks with Simple Architecture Design	See all
Texas	HLP Concat	Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs	See all
Wisconsin	FSGNN	Improving Graph Neural Networks with Simple Architecture Design	See all
Cora Full-supervised	GCNII	Simple and Deep Graph Convolutional Networks	See all
Brazil Air-Traffic	DEMO-Net(weight)	DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification	See all
USA Air-Traffic	DEMO-Net(weight)	DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification	See all
Flickr	DEMO-Net(weight)	DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification	See all
Citeseer Full-supervised	IncepGCN+DropEdge	DropEdge: Towards Deep Graph Convolutional Networks on Node Classification	See all
Pubmed Full-supervised	GraphSAGE+DropEdge	DropEdge: Towards Deep Graph Convolutional Networks on Node Classification	See all
Wiki-Vote	DEMO-Net(weight)	DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification	See all
Europe Air-Traffic	GCN_cheby (Kipf and Welling, 2017)	Semi-Supervised Classification with Graph Convolutional Networks	See all
Coauthor Physics	CoLinkDist	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	See all
Wikipedia	GraphGAN	GraphGAN: Graph Representation Learning with Generative Adversarial Nets	See all
Cora: fixed 20 node per class	DSGCN	Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks	See all
AMZ Photo	CPF-ind-GAT	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework	See all
Facebook	DEMO-Net(weight)	DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification	See all
MAG240M-LSC	R-GAT (NS)	OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs	See all
NELL	DFNet-ATT	DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters	See all
Cora Full	CoLinkDist	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	See all
Amazon Photo	CoLinkDist	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	See all
Amazon Computers	LinkDist	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	See all
Cora Full with Public Split	CoLinkDist	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	See all
MS ACADEMIC	APPNP	Predict then Propagate: Graph Neural Networks meet Personalized PageRank	See all
AIFB	R-GCN	Modeling Relational Data with Graph Convolutional Networks	See all
BGS	RDF2Vec+SVM	RDF2Vec: RDF Graph Embeddings and Their Applications	See all
MUTAG	Path Tree	Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph	See all
AM	R-GCN	Modeling Relational Data with Graph Convolutional Networks	See all
AMZ Comp	GCN (Heat Diffusion)	Diffusion Improves Graph Learning	See all
Wiki	DANMF	Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection	See all
YouTube	LINE	GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding	See all
DBLP	GRACE	Deep Graph Contrastive Representation Learning	See all
Coauthor Phy	GCN-LPA	Unifying Graph Convolutional Neural Networks and Label Propagation	See all
AMZ Computers	CPF-ind-GAT	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework	See all
Cora: fixed 5 node per class	CPF-tra-APPNP	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework	See all
Cora: fixed 10 node per class	CPF-tra-GCNII	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework	See all
20NEWS	GraRep	GraRep: Learning Graph Representations with Global Structural Information	See all
Amazon2M	Cluster-GCN	Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks	See all
Deezer Croatia	GEMSEC 2	GEMSEC: Graph Embedding with Self Clustering	See all
Deezer Hungary	Smooth GEMSEC 2	GEMSEC: Graph Embedding with Self Clustering	See all
Deezer Romania	GEMSEC 2	GEMSEC: Graph Embedding with Self Clustering	See all
CiteSeer with Public Split: fixed 5 nodes per class	GraphMix (GCN)	GraphMix: Improved Training of GNNs for Semi-Supervised Learning	See all
Cora random partition	GraphMix (GCN)	GraphMix: Improved Training of GNNs for Semi-Supervised Learning	See all
Citeseer random partition	GraphMix (GCN)	GraphMix: Improved Training of GNNs for Semi-Supervised Learning	See all
Pubmed random partition	GraphMix (GCN)	GraphMix: Improved Training of GNNs for Semi-Supervised Learning	See all
Bitcoin-OTC	GraphMix (GCN)	GraphMix: Improved Training of GNNs for Semi-Supervised Learning	See all
Bitcoin-Alpha	GraphMix (GCN)	GraphMix: Improved Training of GNNs for Semi-Supervised Learning	See all
CLUSTER	GatedGCN-PE	Benchmarking Graph Neural Networks	See all
PATTERN	GatedGCN-PE	Benchmarking Graph Neural Networks	See all
Crocodile	HLP Concat	Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs	See all

Show all 71 benchmarks

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Datasets

Subtasks

Heterogeneous Node Classification

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

18,536

Paper
Code

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.

Ranked #2 on Graph Classification on CIFAR10 100k

Graph Classification Graph Regression +1

11,555

Paper
Code

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).

Ranked #1 on Node Classification on AMZ Comp

Graph Learning Node Classification

11,555

Paper
Code

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.

Ranked #15 on Node Classification on Citeseer

General Classification Node Classification +1

11,555

Paper
Code

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.

Ranked #4 on Graph Classification on REDDIT-B

Graph Classification Graph Representation Learning +2

11,555

Paper
Code

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

11,555

Paper
Code

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.

Ranked #2 on Node Classification on Cora

General Classification Graph Classification +2

11,555

Paper
Code

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.

Ranked #23 on Node Classification on Cora

Graph Learning Node Classification

7,694

Paper
Code

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.

Ranked #1 on Graph Classification on IPC-grounded