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 976–1000 of 1860 papers

Title	Date	Tasks	Status	Score
Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks	Nov 17, 2021	Knowledge Graph EmbeddingsLink Prediction	CodeCode Available	5
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers	Jul 5, 2023	Anomaly DetectionComputational Efficiency	CodeCode Available	5
Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution	Jan 11, 2022	Graph LearningGraph Neural Network	CodeCode Available	5
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification	Jul 31, 2023	Active LearningGraph Learning	CodeCode Available	5
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors	Sep 27, 2021	Adversarial AttackClustering	CodeCode Available	5
Geometric Scattering Attention Networks	Oct 28, 2020	Graph Representation LearningNode Classification	CodeCode Available	5
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning	Oct 8, 2024	Graph Representation LearningLink Prediction	CodeCode Available	5
Geometry Contrastive Learning on Heterogeneous Graphs	Jun 25, 2022	Contrastive LearningNode Classification	CodeCode Available	5
HyperAggregation: Aggregating over Graph Edges with Hypernetworks	Jul 16, 2024	Graph ClassificationGraph Regression	CodeCode Available	5
HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks	Mar 26, 2024	Graph AttentionNode Classification	CodeCode Available	5
Randomized Schur Complement Views for Graph Contrastive Learning	Jun 6, 2023	Contrastive LearningGraph Classification	CodeCode Available	5
Random Projection Forest Initialization for Graph Convolutional Networks	Feb 22, 2023	graph constructionGraph Embedding	CodeCode Available	5
Random Walk Guided Hyperbolic Graph Distillation	Jan 26, 2025	Continual LearningGraph Learning	CodeCode Available	5
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks	Aug 31, 2023	Node Classification	CodeCode Available	5
HeMI: Multi-view Embedding in Heterogeneous Graphs	Sep 14, 2021	ClusteringLink Prediction	CodeCode Available	5
RDF-star2Vec: RDF-star Graph Embeddings for Data Mining	Dec 25, 2023	BenchmarkingGraph Embedding	CodeCode Available	5
Dynamic Graph Representation Learning via Self-Attention Networks	Dec 22, 2018	General ClassificationGraph Embedding	CodeCode Available	5
Zoom in to where it matters: a hierarchical graph based model for mammogram analysis	Dec 16, 2019	ClassificationGeneral Classification	—Unverified	0
Topological based classification of paper domains using graph convolutional networks	Apr 10, 2019	ClassificationGeneral Classification	—Unverified	0
Top K Enhanced Reinforcement Learning Attacks on Heterogeneous Graph Node Classification	Aug 4, 2024	Node Classification	—Unverified	0
HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport	Jun 3, 2025	Graph Neural NetworkNode Classification	—Unverified	0
2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks	Mar 9, 2021	DescriptiveGeneral Classification	—Unverified	0
2-hop Neighbor Class Similarity (2NCS): A graph structural metric indicative of graph neural network performance	Dec 26, 2022	Graph Neural NetworkNode Classification	—Unverified	0
Effective backdoor attack on graph neural networks in link prediction tasks	Jan 5, 2024	Backdoor AttackGraph Classification	—Unverified	0
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only	Apr 19, 2024	Backdoor AttackGraph Classification	—Unverified	0

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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	NodeNet	Accuracy	86.8	—	Unverified
7	Cleora	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	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