Node Classification

Node Classification is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.

Node Classification models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like Cora, Citeseer, and Pubmed, among others, typically using Accuracy and F1.

( Image credit: Fast Graph Representation Learning With PyTorch Geometric )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 151–175 of 1860 papers

Title	Date	Tasks	Status	Hype
Bayesian Graph Neural Networks with Adaptive Connection Sampling	Jun 7, 2020	Node Classification	CodeCode Available	1
Multi-hop Attention Graph Neural Network	Sep 29, 2020	Graph Neural NetworkGraph Representation Learning	CodeCode Available	1
A Generalization of Transformer Networks to Graphs	Dec 17, 2020	Graph RegressionInductive Bias	CodeCode Available	1
Bayesian Attention Modules	Oct 20, 2020	Image CaptioningMachine Translation	CodeCode Available	1
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	Jun 16, 2021	Node ClassificationNode Property Prediction	CodeCode Available	1
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data	Jul 5, 2021	AttributeDrug Discovery	CodeCode Available	1
Directional Graph Networks	Oct 6, 2020	Data AugmentationGraph Classification	CodeCode Available	1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning	Mar 22, 2023	Dynamic Link PredictionDynamic Node Classification	CodeCode Available	1
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphs	Oct 15, 2023	Few-Shot LearningGraph Learning	CodeCode Available	1
Binary Graph Convolutional Network with Capacity Exploration	Oct 24, 2022	BinarizationNode Classification	CodeCode Available	1
Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models	Jun 17, 2025	AllNode Classification	CodeCode Available	1
Equivariant Hypergraph Diffusion Neural Operators	Jul 14, 2022	Computational EfficiencyNode Classification	CodeCode Available	1
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation	Jun 21, 2021	GPRGraph Neural Network	CodeCode Available	1
Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network	Jul 20, 2022	Graph MiningGraph Neural Network	CodeCode Available	1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs	Feb 26, 2019	Dynamic Link PredictionEdge Classification	CodeCode Available	1
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models	Jul 17, 2023	BenchmarkingGraph Clustering	CodeCode Available	1
A Survey of Adversarial Learning on Graphs	Mar 10, 2020	ClusteringGraph Clustering	CodeCode Available	1
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification	Nov 26, 2024	ClassificationGraph Mining	CodeCode Available	1
CrossWalk: Fairness-enhanced Node Representation Learning	May 6, 2021	FairnessLink Prediction	CodeCode Available	1
Exploring Graph Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation	Feb 26, 2025	BenchmarkingGraph Learning	CodeCode Available	1
A Unified Lottery Ticket Hypothesis for Graph Neural Networks	Feb 12, 2021	Link PredictionNode Classification	CodeCode Available	1
Adaptive Graph Diffusion Networks	Dec 30, 2020	GPULink Prediction	CodeCode Available	1
Factorizable Graph Convolutional Networks	Oct 12, 2020	Graph ClassificationGraph Regression	CodeCode Available	1
Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information	Sep 3, 2020	AttributeFairness	CodeCode Available	1
Confidence-Based Feature Imputation for Graphs with Partially Known Features	May 26, 2023	Graph LearningImputation	CodeCode Available	1

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All datasets Citeseer Pubmed Cora CiteSeer with Public Split: fixed 20 nodes per class PubMed with Public Split: fixed 20 nodes per class Cora with Public Split: fixed 20 nodes per class PPI Coauthor CS Cora (0.5%)Cora (1%)Cora (3%)PubMed (0.03%)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	NodeNet	Accuracy	80.09	—	Unverified
2	SplineCNN	Accuracy	79.2	—	Unverified
3	PathNet	Accuracy (%)	77.98	—	Unverified
4	3ference	Accuracy	76.33	—	Unverified
5	MMA	Accuracy	76.3	—	Unverified
6	PPNP	Accuracy	75.83	—	Unverified
7	CoLinkDist	Accuracy	75.79	—	Unverified
8	CoLinkDistMLP	Accuracy	75.77	—	Unverified
9	APPNP	Accuracy	75.73	—	Unverified
10	Cleora	Accuracy	75.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	NodeNet	Accuracy	90.21	—	Unverified
2	CoLinkDist	Accuracy	89.58	—	Unverified
3	CoLinkDistMLP	Accuracy	89.53	—	Unverified
4	PathNet	Accuracy (%)	88.92	—	Unverified
5	3ference	Accuracy	88.9	—	Unverified
6	SplineCNN	Accuracy	88.88	—	Unverified
7	LinkDist	Accuracy	88.86	—	Unverified
8	LinkDistMLP	Accuracy	88.79	—	Unverified
9	PairE	F1	88.57	—	Unverified
10	GCN + Mixup	Accuracy	87.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LinkDist	Accuracy	88.24	—	Unverified
2	CoLinkDist	Accuracy	87.89	—	Unverified
3	3ference	Accuracy	87.78	—	Unverified
4	LinkDistMLP	Accuracy	87.58	—	Unverified
5	CoLinkDistMLP	Accuracy	87.54	—	Unverified
6	Cleora	Accuracy	86.8	—	Unverified
7	NodeNet	Accuracy	86.8	—	Unverified
8	MMA	Accuracy	85.8	—	Unverified
9	GResNet(GAT)	Accuracy	85.5	—	Unverified
10	DifNet	Accuracy	85.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	OGC	Accuracy	77.5	—	Unverified
2	LDS-GNN	Accuracy	75	—	Unverified
3	CPF-tra-APPNP	Accuracy	74.6	—	Unverified
4	G3NN	Accuracy	74.5	—	Unverified
5	GGCM	Accuracy	74.2	—	Unverified
6	GEM	Accuracy	74.2	—	Unverified
7	Truncated Krylov	Accuracy	73.86	—	Unverified
8	SSGC	Accuracy	73.6	—	Unverified
9	OKDEEM	Accuracy	73.53	—	Unverified
10	GCNII	Accuracy	73.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	OGC	Accuracy	83.4	—	Unverified
2	CPF-tra-GCNII	Accuracy	83.2	—	Unverified
3	DSGCN	Accuracy	81.9	—	Unverified
4	SuperGAT MX	Accuracy	81.7	—	Unverified
5	Truncated Krylov	Accuracy	81.7	—	Unverified
6	G-APPNP	Accuracy	80.95	—	Unverified
7	GGCM	Accuracy	80.8	—	Unverified
8	GCN(predicted-targets)	Accuracy	80.42	—	Unverified
9	SSGC	Accuracy	80.4	—	Unverified
10	GCNII	Accuracy	80.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	OGC	Accuracy	86.9	—	Unverified
2	GCN-TV	Accuracy	86.3	—	Unverified
3	GCNII	Accuracy	85.5	—	Unverified
4	CPF-ind-APPNP	Accuracy	85.3	—	Unverified
5	AIR-GCN	Accuracy	84.7	—	Unverified
6	H-GCN	Accuracy	84.5	—	Unverified
7	G-APPNP	Accuracy	84.31	—	Unverified
8	SuperGAT MX	Accuracy	84.3	—	Unverified
9	DSGCN	Accuracy	84.2	—	Unverified
10	LDS-GNN	Accuracy	84.1	—	Unverified