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 751–775 of 1860 papers

Title	Date	Tasks	Status	Score
Learning on Large Graphs using Intersecting Communities	May 31, 2024	Graph LearningLEMMA	CodeCode Available	5
LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework	May 22, 2024	Graph Neural NetworkLanguage Modeling	CodeCode Available	5
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation	Jan 18, 2024	Graph LearningNode Classification	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
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs	Apr 11, 2022	Node Classification	CodeCode Available	5
Graph Few-shot Learning via Knowledge Transfer	Oct 7, 2019	Few-Shot LearningGeneral Classification	CodeCode Available	5
Graph Few-shot Learning with Task-specific Structures	Oct 21, 2022	ClassificationFew-Shot Learning	CodeCode Available	5
Addressing the Impact of Localized Training Data in Graph Neural Networks	Jul 24, 2023	Anomaly DetectionGraph Generation	CodeCode Available	5
Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex	Apr 16, 2025	Link PredictionNode Classification	CodeCode Available	5
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding	Apr 6, 2021	Link PredictionNetwork Embedding	CodeCode Available	5
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs	Mar 20, 2018	General ClassificationNode Classification	CodeCode Available	5
AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification	Dec 11, 2024	Data AugmentationNode Classification	CodeCode Available	5
Graph Fourier Transformer with Structure-Frequency Information	Apr 28, 2025	Graph ClassificationInductive Bias	CodeCode Available	5
Fusion Graph Convolutional Networks	May 31, 2018	General ClassificationNode Classification	CodeCode Available	5
Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision	Mar 7, 2022	Node ClassificationSelf-Supervised Learning	CodeCode Available	5
GraphGAN: Graph Representation Learning with Generative Adversarial Nets	Nov 22, 2017	Computational EfficiencyGraph Representation Learning	CodeCode Available	5
Connector 0.5: A unified framework for graph representation learning	Apr 25, 2023	Graph EmbeddingGraph Generation	CodeCode Available	5
Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences	Oct 1, 2021	Link PredictionNode Classification	CodeCode Available	5
IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors	May 2, 2024	Data AugmentationNode Classification	CodeCode Available	5
iN2V: Bringing Transductive Node Embeddings to Inductive Graphs	Jun 5, 2025	Node ClassificationRepresentation Learning	CodeCode Available	5
Improving Your Graph Neural Networks: A High-Frequency Booster	Oct 15, 2022	Node ClassificationVocal Bursts Intensity Prediction	CodeCode Available	5
From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis	Nov 17, 2024	Graph RegressionHeterogeneous Node Classification	CodeCode Available	5
Independent Distribution Regularization for Private Graph Embedding	Aug 16, 2023	AttributeGraph Embedding	CodeCode Available	5
Improving Graph Neural Networks by Learning Continuous Edge Directions	Oct 18, 2024	Node Classification	CodeCode Available	5
From Node Embedding To Community Embedding	Oct 31, 2016	Graph EmbeddingNode 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