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 726–750 of 1860 papers

Title	Date	Tasks	Status	Score
Deepened Graph Auto-Encoders Help Stabilize and Enhance Link Prediction	Mar 21, 2021	BenchmarkingClustering	CodeCode Available	5
Kernel Node Embeddings	Sep 8, 2019	Link PredictionNode Classification	CodeCode Available	5
Dynamic Embedding on Textual Networks via a Gaussian Process	Oct 5, 2019	Link PredictionNetwork Embedding	CodeCode Available	5
Gaussian Embedding of Large-scale Attributed Graphs	Dec 2, 2019	Graph EmbeddingLink Prediction	CodeCode Available	5
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training	Dec 24, 2023	Graph Neural NetworkLink Prediction	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
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation	Jan 18, 2024	Graph LearningNode Classification	CodeCode Available	5
Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex	Apr 16, 2025	Link PredictionNode Classification	CodeCode Available	5
Attention-Driven Metapath Encoding in Heterogeneous Graphs	Dec 30, 2024	Node 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
Continuous Graph Neural Networks	Dec 2, 2019	Node Classification	CodeCode Available	5
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs	Apr 11, 2022	Node Classification	CodeCode Available	5
IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors	May 2, 2024	Data AugmentationNode Classification	CodeCode Available	5
k-hop Graph Neural Networks	Jul 13, 2019	General ClassificationGraph Classification	CodeCode Available	5
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks	Nov 17, 2019	Node Classification	CodeCode Available	5
LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information Networks	Feb 11, 2021	Anomaly DetectionFraud Detection	CodeCode Available	5
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification	Dec 11, 2024	Computational Efficiency	CodeCode Available	5
Improving Graph Neural Networks by Learning Continuous Edge Directions	Oct 18, 2024	Node Classification	CodeCode Available	5
Calibrating and Improving Graph Contrastive Learning	Jan 27, 2021	Contrastive LearningGraph Clustering	CodeCode Available	5
Graph Convolutional Neural Networks via Scattering	Mar 31, 2018	Node Classification	CodeCode Available	5
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes	Dec 5, 2022	Computational EfficiencyGraph Representation Learning	CodeCode Available	5
Improving Your Graph Neural Networks: A High-Frequency Booster	Oct 15, 2022	Node ClassificationVocal Bursts Intensity Prediction	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
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	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