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

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Showing 1276–1300 of 1860 papers

Title	Date	Tasks	Status
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs	Apr 29, 2025	Federated LearningGraph Learning	—Unverified
Few-shot Classification on Graphs with Structural Regularized GCNs	May 1, 2019	ClassificationFew-Shot Learning	—Unverified
FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths	Feb 22, 2025	Graph EmbeddingLink Prediction	—Unverified
Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification	Feb 20, 2023	Node Classification	—Unverified
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning	Dec 10, 2024	Contrastive LearningGraph Representation Learning	—Unverified
Flexible infinite-width graph convolutional networks and the importance of representation learning	Feb 9, 2024	ClassificationGraph Classification	—Unverified
Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation	Nov 28, 2022	Data AugmentationNode Classification	—Unverified
FMGNN: Fused Manifold Graph Neural Network	Apr 3, 2023	Graph Neural NetworkGraph Representation Learning	—Unverified
FMP: Toward Fair Graph Message Passing against Topology Bias	Feb 8, 2022	Contrastive LearningFairness	—Unverified
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification	Oct 25, 2022	Adversarial RobustnessData Poisoning	—Unverified
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey	May 13, 2020	Dynamic Link PredictionEpidemiology	—Unverified
Framelet Message Passing	Feb 28, 2023	Node Classification	—Unverified
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning	Jun 21, 2024	Contrastive LearningData Augmentation	—Unverified
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs	Jul 17, 2023	Graph Representation LearningNode Classification	—Unverified
From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks	Jul 12, 2022	Node Classification	—Unverified
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation	Jul 1, 2020	Node Classification	—Unverified
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning	May 24, 2025	Link PredictionNode Classification	—Unverified
GAGE: Geometry Preserving Attributed Graph Embeddings	Nov 3, 2020	AttributeLink Prediction	—Unverified
GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck	Nov 7, 2024	Graph structure learningNode Classification	—Unverified
GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs	Nov 3, 2020	Graph AttentionGraph Neural Network	—Unverified
Game-theoretic Counterfactual Explanation for Graph Neural Networks	Feb 8, 2024	counterfactualCounterfactual Explanation	—Unverified
GANExplainer: GAN-based Graph Neural Networks Explainer	Dec 30, 2022	Decision MakingGenerative Adversarial Network	—Unverified
GANN: Graph Alignment Neural Network for Semi-Supervised Learning	Mar 14, 2023	AttributeNode Classification	—Unverified
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks	Jul 25, 2021	Graph ClassificationNode Classification	—Unverified
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs	Oct 11, 2021	ClassificationDynamic Node Classification	—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	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