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 1626–1650 of 1860 papers

Title	Date	Tasks	Status
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations	Dec 8, 2021	Graph Representation LearningMulti-Label Classification	—Unverified
Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks	Dec 18, 2024	Node Classificationregression	—Unverified
Optimal Inference in Contextual Stochastic Block Models	Jun 6, 2023	Community DetectionNode Classification	—Unverified
Optimality of Message-Passing Architectures for Sparse Graphs	May 17, 2023	Graph Neural NetworkNode Classification	—Unverified
Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks	Jan 21, 2025	Fraud DetectionGraph Representation Learning	—Unverified
Ordinary differential equations on graph networks	Sep 25, 2019	Graph ClassificationGraph Neural Network	—Unverified
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization	Jan 31, 2023	Node Classification	—Unverified
Out-of-Distribution Detection in Heterogeneous Graphs via Energy Propagation	Apr 29, 2025	Node ClassificationOut-of-Distribution Detection	—Unverified
Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations	Dec 30, 2024	Graph LearningGraph Neural Network	—Unverified
Overlap-aware meta-learning attention to enhance hypergraph neural networks for node classification	Mar 11, 2025	Meta-LearningNode Classification	—Unverified
Over-Squashing in Graph Neural Networks: A Comprehensive survey	Aug 29, 2023	Link PredictionNode Classification	—Unverified
Pair-view Unsupervised Graph Representation Learning	Dec 11, 2020	Graph Representation LearningLink Prediction	—Unverified
PanRep: Universal node embeddings for heterogeneous graphs	Sep 28, 2020	Graph Neural NetworkLink Prediction	—Unverified
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis	May 19, 2022	Graph ClassificationLink Prediction	—Unverified
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation	Aug 10, 2024	Graph LearningGraph Representation Learning	—Unverified
Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics	May 19, 2025	Domain AdaptationGRAPH DOMAIN ADAPTATION	—Unverified
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks	Mar 3, 2022	Community DetectionLink Prediction	—Unverified
Permutohedral-GCN: Graph Convolutional Networks with Global Attention	Mar 2, 2020	Node Classification	—Unverified
Personalized Layer Selection for Graph Neural Networks	Jan 24, 2025	Metric LearningNode Classification	—Unverified
PersonaSAGE: A Multi-Persona Graph Neural Network	Dec 28, 2022	Graph Neural NetworkLink Prediction	—Unverified
Perturbation Ontology based Graph Attention Networks	Nov 27, 2024	Graph AttentionGraph Representation Learning	—Unverified
PI-GNN: Towards Robust Semi-Supervised Node Classification against Noisy Labels	Sep 29, 2021	Graph MiningGraph Neural Network	—Unverified
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions	Sep 25, 2019	Community DetectionGraph Embedding	—Unverified
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding	Dec 19, 2023	Graph EmbeddingInductive Bias	—Unverified
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks	Jun 25, 2023	graph partitioningNode 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	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