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 1226–1250 of 1860 papers

Title	Date	Tasks	Status
Embedding Node Structural Role Identity Using Stress Majorization	Sep 14, 2021	Node Classification	—Unverified
Empty Category Detection using Path Features and Distributed Case Frames	Sep 1, 2015	Machine TranslationNode Classification	—Unverified
Encoder Embedding for General Graph and Node Classification	May 24, 2024	ClassificationNode Classification	—Unverified
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models	Jul 19, 2024	Active LearningKnowledge Graphs	—Unverified
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning	Jan 21, 2022	Contrastive LearningGraph Representation Learning	—Unverified
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach	Nov 20, 2022	Graph Representation LearningNeural Architecture Search	—Unverified
Enhancing the Resilience of Graph Neural Networks to Topological Perturbations in Sparse Graphs	Jun 5, 2024	Link PredictionNode Classification	—Unverified
EPINE: Enhanced Proximity Information Network Embedding	Mar 4, 2020	Link PredictionNetwork Embedding	—Unverified
Equivariant Heterogeneous Graph Networks	Sep 29, 2021	Link PredictionNode Classification	—Unverified
Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings	Sep 9, 2021	Link PredictionNode Classification	—Unverified
ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification	Apr 15, 2022	Event Causality IdentificationNode Classification	—Unverified
Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification	Nov 29, 2022	ClassificationNode Classification	—Unverified
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning	Feb 9, 2024	Data AugmentationGraph Generation	—Unverified
Expanding Label Sets for Graph Convolutional Networks	Dec 18, 2019	ClusteringGeneral Classification	—Unverified
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation	Dec 28, 2023	Adversarial AttackNode Classification	—Unverified
Explainability-based Backdoor Attacks Against Graph Neural Networks	Apr 8, 2021	Backdoor AttackNode Classification	—Unverified
Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification	Feb 2, 2020	General ClassificationNode Classification	—Unverified
Explaining and Adapting Graph Conditional Shift	Jun 5, 2023	Domain AdaptationGraph Classification	—Unverified
Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification	Jul 27, 2021	Graph Neural NetworkNode Classification	—Unverified
Exploiting Edge Features for Graph Neural Networks	Jun 1, 2019	General ClassificationGraph Attention	—Unverified
Exploiting Edge Features in Graph Neural Networks	Sep 7, 2018	General ClassificationGraph Attention	—Unverified
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning	Jun 30, 2021	Graph AttentionGraph Learning	—Unverified
Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph	May 13, 2025	Node ClassificationTAG	—Unverified
Exploring Edge Disentanglement for Node Classification	Feb 23, 2022	ClassificationDisentanglement	—Unverified
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification	Apr 23, 2019	General ClassificationGraph Learning	—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	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	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	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