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 801–825 of 1860 papers

Title	Date	Tasks	Status
A Graph Data Augmentation Strategy with Entropy Preservation	Jul 13, 2021	Data AugmentationNode Classification	—Unverified
Higher Order Graph Attention Probabilistic Walk Networks	Nov 18, 2024	DiversityGraph Attention	—Unverified
Higher-order Weighted Graph Convolutional Networks	Nov 11, 2019	Node Classification	—Unverified
Graph Neural Networks with a Distribution of Parametrized Graphs	Oct 25, 2023	Graph RegressionNode Classification	—Unverified
Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise	Nov 6, 2024	Graph ReconstructionNode Classification	—Unverified
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols	Jun 11, 2025	Data PoisoningGraph Learning	—Unverified
HopGAT: Hop-aware Supervision Graph Attention Networks for Sparsely Labeled Graphs	Apr 9, 2020	General ClassificationGraph Attention	—Unverified
Graph Neural Networks with Feature and Structure Aware Random Walk	Nov 19, 2021	Node ClassificationRepresentation Learning	—Unverified
Convolutional Networks on Enhanced Message-Passing Graph Improve Semi-Supervised Classification with Few Labels	Sep 29, 2021	Graph EmbeddingNode Classification	—Unverified
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification	Feb 5, 2022	ClassificationGraph Classification	—Unverified
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling	Jul 7, 2022	Node Classification	—Unverified
Graph Neural Tangent Kernel: Convergence on Large Graphs	Jan 25, 2023	Node ClassificationNode Regression	—Unverified
Hierarchical Randomized Smoothing	Oct 24, 2023	Node Classification	—Unverified
Robust Graph Data Learning via Latent Graph Convolutional Representation	Apr 26, 2019	Graph LearningNode Classification	—Unverified
GCN-SL: Graph Convolutional Network with Structure Learning for Disassortative Graphs	Sep 29, 2021	Node ClassificationRepresentation Learning	—Unverified
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs	Oct 11, 2021	ClassificationDynamic Node Classification	—Unverified
Attributed Graph Learning with 2-D Graph Convolution	Sep 25, 2019	AttributeGraph Learning	—Unverified
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks	Jul 25, 2021	Graph ClassificationNode Classification	—Unverified
A Graph Convolutional Network Composition Framework for Semi-supervised Classification	Apr 8, 2020	ClassificationGeneral Classification	—Unverified
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN	Apr 3, 2020	AttributeLink Prediction	—Unverified
Hierarchical Model Selection for Graph Neural Netoworks	Dec 1, 2022	Classificationmodel	—Unverified
Graph Polynomial Convolution Models for Node Classification of Non-Homophilous Graphs	Sep 12, 2022	Generalization BoundsNode Classification	—Unverified
HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks	Jan 22, 2025	Link PredictionNode Classification	—Unverified
Graph Positional Autoencoders as Self-supervised Learners	May 29, 2025	Graph Property PredictionMissing Elements	—Unverified
Attention Models with Random Features for Multi-layered Graph Embeddings	Oct 2, 2018	Network EmbeddingNode Classification	—Unverified

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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	TransGNN	1:1 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