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 901–925 of 1860 papers

Title	Date	Tasks	Status
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs	Jul 17, 2023	Graph Representation LearningNode Classification	—Unverified
Heterophilous Distribution Propagation for Graph Neural Networks	May 31, 2024	Contrastive LearningGraph Mining	—Unverified
Connecting Graph Convolutional Networks and Graph-Regularized PCA	Jun 22, 2020	Node Classification	—Unverified
Heterophily-Based Graph Neural Network for Imbalanced Classification	Oct 12, 2023	ClassificationGraph Neural Network	—Unverified
A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks	Mar 23, 2022	ClassificationHierarchical Multi-label Classification	—Unverified
HetFS: A Method for Fast Similarity Search with Ad-hoc Meta-paths on Heterogeneous Information Networks	Feb 22, 2025	Graph Neural NetworkLink Prediction	—Unverified
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks	Oct 23, 2023	Node Classification	—Unverified
Inductive Graph Few-shot Class Incremental Learning	Nov 11, 2024	class-incremental learningClass Incremental Learning	—Unverified
Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention	Feb 5, 2020	ArticlesFake News Detection	—Unverified
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness	Jun 7, 2023	MULTI-VIEW LEARNINGNode Classification	—Unverified
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning	Jun 21, 2024	Contrastive LearningData Augmentation	—Unverified
Conformal Inductive Graph Neural Networks	Jul 12, 2024	Conformal PredictionNode Classification	—Unverified
A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids	Dec 7, 2022	Cyber Attack DetectionGraph Neural Network	—Unverified
Dual Node and Edge Fairness-Aware Graph Partition	Jun 16, 2023	FairnessLink Prediction	—Unverified
AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer	Apr 16, 2024	AttributeNode Classification	—Unverified
Hierarchical Model Selection for Graph Neural Netoworks	Dec 1, 2022	Classificationmodel	—Unverified
Hierarchical Randomized Smoothing	Oct 24, 2023	Node Classification	—Unverified
Inductive Linear Probing for Few-shot Node Classification	Jun 14, 2023	ClassificationFew-Shot Learning	—Unverified
HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks	Jan 22, 2025	Link PredictionNode Classification	—Unverified
Incremental Learning on Growing Graphs	Jan 1, 2021	Graph LearningIncremental Learning	—Unverified
DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks	Mar 12, 2021	Link PredictionNode Classification	—Unverified
Higher Order Graph Attention Probabilistic Walk Networks	Nov 18, 2024	DiversityGraph Attention	—Unverified
Conditional Shift-Robust Conformal Prediction for Graph Neural Network	May 20, 2024	Conformal PredictionGraph Neural Network	—Unverified
Conditional Local Feature Encoding for Graph Neural Networks	May 8, 2024	Graph ClassificationGraph Regression	—Unverified
Framelet Message Passing	Feb 28, 2023	Node 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	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