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 601–625 of 1860 papers

Title	Date	Tasks	Status	Score
Calibrating and Improving Graph Contrastive Learning	Jan 27, 2021	Contrastive LearningGraph Clustering	CodeCode Available	5
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification	Jun 5, 2019	General ClassificationGraph Classification	CodeCode Available	5
Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark	Jun 14, 2025	BenchmarkingGraph Learning	CodeCode Available	5
iN2V: Bringing Transductive Node Embeddings to Inductive Graphs	Jun 5, 2025	Node ClassificationRepresentation Learning	CodeCode Available	5
DeltaGNN: Graph Neural Network with Information Flow Control	Jan 10, 2025	Graph Neural NetworkNode Classification	CodeCode Available	5
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction	Jan 28, 2024	Node Classification	CodeCode Available	5
A Capsule Network-based Model for Learning Node Embeddings	Nov 12, 2019	General ClassificationNode Classification	CodeCode Available	5
Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks	Aug 28, 2019	General ClassificationGraph Embedding	CodeCode Available	5
Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation	Jul 4, 2019	Graph ClassificationGraph Representation Learning	CodeCode Available	5
DEGREE: Decomposition Based Explanation For Graph Neural Networks	May 22, 2023	Graph ClassificationNode Classification	CodeCode Available	5
Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers	Nov 1, 2022	FairnessImputation	CodeCode Available	5
Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization	Nov 10, 2023	Node ClassificationOut of Distribution (OOD) Detection	CodeCode Available	5
Article Classification with Graph Neural Networks and Multigraphs	Sep 20, 2023	ArticlesClassification	CodeCode Available	5
Improving Graph Neural Networks by Learning Continuous Edge Directions	Oct 18, 2024	Node Classification	CodeCode Available	5
IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors	May 2, 2024	Data AugmentationNode Classification	CodeCode Available	5
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs	Jul 7, 2023	Graph LearningGraph Neural Network	CodeCode Available	5
Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision	Mar 7, 2022	Node ClassificationSelf-Supervised Learning	CodeCode Available	5
DeepWalk: Online Learning of Social Representations	Mar 26, 2014	Anomaly DetectionLanguage Modeling	CodeCode Available	5
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees	May 7, 2022	Graph ClassificationNode Classification	CodeCode Available	5
Hypergraph Neural Networks for Hypergraph Matching	Jan 1, 2021	Graph MatchingHypergraph Matching	CodeCode Available	5
Addressing the Impact of Localized Training Data in Graph Neural Networks	Jul 24, 2023	Anomaly DetectionGraph Generation	CodeCode Available	5
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and Classification	Feb 11, 2019	ClassificationGeneral Classification	CodeCode Available	5
Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference	Oct 31, 2020	Graph EmbeddingLink Prediction	CodeCode Available	5
Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic Decomposition	Apr 16, 2025	Contrastive LearningGraph Embedding	CodeCode Available	5
Hyperedge Anomaly Detection with Hypergraph Neural Network	Dec 7, 2024	Anomaly DetectionLink Prediction	CodeCode Available	5

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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	GEM	Accuracy	74.2	—	Unverified
6	GGCM	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	Truncated Krylov	Accuracy	81.7	—	Unverified
5	SuperGAT MX	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