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	Score
Investigating the Interplay between Features and Structures in Graph Learning	Aug 18, 2023	Graph LearningInductive Bias	CodeCode Available	5
It Takes a Graph to Know a Graph: Rewiring for Homophily with a Reference Graph	May 18, 2025	Node Classification	CodeCode Available	5
IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors	May 2, 2024	Data AugmentationNode Classification	CodeCode Available	5
BeMap: Balanced Message Passing for Fair Graph Neural Network	Jun 7, 2023	FairnessGraph Neural Network	CodeCode Available	5
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative sampling	Sep 1, 2023	Contrastive LearningGraph Representation Learning	CodeCode Available	5
Graph Neural Networks with convolutional ARMA filters	Jan 5, 2019	ClassificationGeneral Classification	CodeCode Available	5
Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex	Apr 16, 2025	Link PredictionNode Classification	CodeCode Available	5
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning	Dec 22, 2021	Link PredictionLink Property Prediction	CodeCode Available	5
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs	Apr 11, 2022	Node Classification	CodeCode Available	5
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters	Oct 24, 2019	Document ClassificationGeneral Classification	CodeCode Available	5
Asymptotics of _2 Regularized Network Embeddings	Jan 5, 2022	Link PredictionNode Classification	CodeCode Available	5
Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences	Oct 1, 2021	Link PredictionNode Classification	CodeCode Available	5
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation	Jan 18, 2024	Graph LearningNode Classification	CodeCode Available	5
Fisher-Bures Adversary Graph Convolutional Networks	Mar 11, 2019	Graph Neural NetworkNode Classification	CodeCode Available	5
Independent Distribution Regularization for Private Graph Embedding	Aug 16, 2023	AttributeGraph Embedding	CodeCode Available	5
Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph	Aug 22, 2019	Deep LearningKnowledge Graphs	CodeCode Available	5
Information Extraction from Visually Rich Documents Using Directed Weighted Graph Neural Network	Sep 11, 2024	Document Layout Analysisdocument understanding	CodeCode Available	5
Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network	Mar 26, 2024	Graph LearningGraph Neural Network	CodeCode Available	5
Calibrating and Improving Graph Contrastive Learning	Jan 27, 2021	Contrastive LearningGraph Clustering	CodeCode Available	5
Few-shot Node Classification with Extremely Weak Supervision	Jan 6, 2023	ClassificationMeta-Learning	CodeCode Available	5
AGALE: A Graph-Aware Continual Learning Evaluation Framework	Jun 3, 2024	Continual LearningGraph Learning	CodeCode Available	5
Improving Graph Neural Networks by Learning Continuous Edge Directions	Oct 18, 2024	Node Classification	CodeCode Available	5
Decoupled Subgraph Federated Learning	Feb 29, 2024	Federated LearningNode Classification	CodeCode Available	5
Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation	Jul 4, 2019	Graph ClassificationGraph Representation Learning	CodeCode Available	5
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction	Jan 28, 2024	Node Classification	CodeCode Available	5

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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