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 1676–1700 of 1860 papers

Title	Date	Tasks	Status
Chasing Fairness in Graphs: A GNN Architecture Perspective	Dec 19, 2023	AttributeContrastive Learning	CodeCode Available
Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling	May 19, 2021	ClusteringGPU	CodeCode Available
Local2Global: Scaling global representation learning on graphs via local training	Jul 26, 2021	Graph ReconstructionGraph Representation Learning	CodeCode Available
Local, global and scale-dependent node roles	May 26, 2021	Graph LearningGraph Neural Network	CodeCode Available
Search Efficient Binary Network Embedding	Jan 14, 2019	AttributeNetwork Embedding	CodeCode Available
Privacy-Preserving Graph Embedding based on Local Differential Privacy	Oct 17, 2023	Graph EmbeddingLink Prediction	CodeCode Available
Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing	Sep 5, 2020	Graph MiningGraph Reconstruction	CodeCode Available
LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework	May 22, 2024	Graph Neural NetworkLanguage Modeling	CodeCode Available
Framework for Designing Filters of Spectral Graph Convolutional Neural Networks in the Context of Regularization Theory	Sep 29, 2020	Graph LearningNode Classification	CodeCode Available
Fisher-Bures Adversary Graph Convolutional Networks	Mar 11, 2019	Graph Neural NetworkNode Classification	CodeCode Available
Few-shot Node Classification with Extremely Weak Supervision	Jan 6, 2023	ClassificationMeta-Learning	CodeCode Available
Decoupled Subgraph Federated Learning	Feb 29, 2024	Federated LearningNode Classification	CodeCode Available
Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation	Aug 29, 2023	Graph Neural NetworkNode Classification	CodeCode Available
Weighted Graph Structure Learning with Attention Denoising for Node Classification	Mar 15, 2025	ClassificationDenoising	CodeCode Available
LPGNet: Link Private Graph Networks for Node Classification	May 6, 2022	ClassificationNode Classification	CodeCode Available
Federated Graph Learning with Structure Proxy Alignment	Aug 18, 2024	Federated LearningFraud Detection	CodeCode Available
Feature Selection: Key to Enhance Node Classification with Graph Neural Networks	Jan 25, 2023	feature selectionInductive Bias	CodeCode Available
Lying Graph Convolution: Learning to Lie for Node Classification Tasks	May 2, 2024	Node Classification	CodeCode Available
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks	Jun 24, 2021	Feature CorrelationGraph Classification	CodeCode Available
FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping	Oct 21, 2021	FairnessGraph Attention	CodeCode Available
Fast Online Node Labeling for Very Large Graphs	May 25, 2023	Node ClassificationTransductive Learning	CodeCode Available
Certifiable Robustness and Robust Training for Graph Convolutional Networks	Jun 28, 2019	Node Classification	CodeCode Available
A Scalable Multiclass Algorithm for Node Classification	Dec 19, 2011	ClassificationNode Classification	CodeCode Available
MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage	Jan 23, 2024	AttributeFairness	CodeCode Available
Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification	Oct 22, 2024	Node Classification	CodeCode Available

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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	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