Node Classification Edit Task

Graphs

284

papers with code

benchmarks

datasets

About
Edit

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 METHOD	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%)	Truncated Krylov	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks	See all
Cora (1%)	Truncated Krylov	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks	See all
Cora (3%)	Truncated Krylov	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks	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
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
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
Coauthor CS	GCN-LPA	Unifying Graph Convolutional Neural Networks and Label Propagation	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
Chameleon	GGCN	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks	See all
Squirrel	GGCN	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks	See all
Actor	GGCN	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks	See all
Cornell	GGCN	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks	See all
Texas	GGCN	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks	See all
Wisconsin	GGCN	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks	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
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
AMZ Photo	JK (Heat Diffusion)	Diffusion Improves Graph Learning	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
Coauthor Physics	GraphMix (GCN)	GraphMix: Improved Training of GNNs for Semi-Supervised Learning	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
AMZ Computers	DAGNN (Ours)	Towards Deeper Graph Neural Networks	See all
Cora: fixed 5 node per class	GraphMix (GCN)	GraphMix: Improved Training of GNNs for Semi-Supervised Learning	See all
Cora: fixed 10 node per class	GraphMix (GCN)	GraphMix: Improved Training of GNNs for Semi-Supervised Learning	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
ogbn-products	UniMP	Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification	See all
ogbn-proteins	unimp	Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification	See all
ogbn-arxiv	unimp	Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification	See all

Subtasks

Heterogeneous Node Classification

1 benchmark

4 papers with code

Datasets

Cora

Citeseer

Pubmed

PPI

See all 19 datasets

Latest papers with code

CrossWalk: Fairness-enhanced Node Representation Learning

6 May 2021 • ahmadkhajehnejad/CrossWalk

The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular and academic attention.

FAIRNESS LINK PREDICTION NODE CLASSIFICATION REPRESENTATION LEARNING

06 May 2021

Paper
Code

Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

16 Apr 2021 • safe-graph/RioGNN •

A reinforced relation-aware neighbor selection mechanism is developed to choose the most similar neighbors of a targeting node within a relation before aggregating all neighborhood information from different relations to obtain the eventual node embedding.

NODE CLASSIFICATION REPRESENTATION LEARNING

16 Apr 2021

Paper
Code

Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

16 Apr 2021 • RingBDStack/HAE •

Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors.

NODE CLASSIFICATION NODE CLUSTERING REPRESENTATION LEARNING

16 Apr 2021

Paper
Code

mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding

6 Apr 2021 • XinyiZ001/mSHINE •

To address this issue, a novel meta-path-based HIN representation learning framework named mSHINE is designed to simultaneously learn multiple node representations for different meta-paths.

LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

06 Apr 2021

Paper
Code

OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

17 Mar 2021 • snap-stanford/ogb •

We show that the expressive models significantly outperform simple scalable baselines, indicating an opportunity for dedicated efforts to further improve graph ML at scale.

Ranked #1 on Node Classification on MAG240M-LSC

GRAPH LEARNING GRAPH REGRESSION KNOWLEDGE GRAPHS LINK PREDICTION NODE CLASSIFICATION

926

17 Mar 2021

Paper
Code

GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks

16 Mar 2021 • TianxiangZhao/GraphSmote •

This task is non-trivial, as previous synthetic minority over-sampling algorithms fail to provide relation information for newly synthesized samples, which is vital for learning on graphs.

CLASSIFICATION GRAPH LEARNING NODE CLASSIFICATION

16 Mar 2021

Paper
Code

Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs

5 Mar 2021 • lorenzodallamico/NishimoriBetheHessian

This article unveils a new relation between the Nishimori temperature parametrizing a distribution P and the Bethe free energy on random Erdos-Renyi graphs with edge weights distributed according to P. Estimating the Nishimori temperature being a task of major importance in Bayesian inference problems, as a practical corollary of this new relation, a numerical method is proposed to accurately estimate the Nishimori temperature from the eigenvalues of the Bethe Hessian matrix of the weighted graph.

BAYESIAN INFERENCE CLASSIFICATION NODE CLASSIFICATION

05 Mar 2021

Paper
Code

CogDL: An Extensive Toolkit for Deep Learning on Graphs

1 Mar 2021 • THUDM/cogdl •

Most of the graph embedding methods learn node-level or graph-level representations in an unsupervised way and preserves the graph properties such as structural information, while graph neural networks capture node features and work in semi-supervised or self-supervised settings.

GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION RECOMMENDATION SYSTEMS

655

01 Mar 2021

Paper
Code