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 1601–1625 of 1860 papers

Title	Date	Tasks	Status
Node Copying: A Random Graph Model for Effective Graph Sampling	Aug 4, 2022	Graph SamplingNode Classification	—Unverified
Node Embedding for Homophilous Graphs with ARGEW: Augmentation of Random walks by Graph Edge Weights	Aug 11, 2023	Node Classification	—Unverified
Node Injection Attacks on Graphs via Reinforcement Learning	Sep 14, 2019	Node Classificationreinforcement-learning	—Unverified
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning	Aug 9, 2024	Graph LearningGraph Representation Learning	—Unverified
Node Masking: Making Graph Neural Networks Generalize and Scale Better	Jan 17, 2020	Node ClassificationTransductive Learning	—Unverified
NodeNet: A Graph Regularised Neural Network for Node Classification	Jun 16, 2020	ClassificationGeneral Classification	—Unverified
NodeReg: Mitigating the Imbalance and Distribution Shift Effects in Semi-Supervised Node Classification via Norm Consistency	Mar 5, 2025	Node Classification	—Unverified
Node Representation Learning for Directed Graphs	Oct 22, 2018	General ClassificationGraph Reconstruction	—Unverified
NODE-SELECT: A FLEXIBLE GRAPH NEURAL NETWORK BASED ON REALISTIC PROPAGATION SCHEME	Jan 1, 2021	Graph Neural NetworkNode Classification	—Unverified
Node-Specific Space Selection via Localized Geometric Hyperbolicity in Graph Neural Networks	Mar 3, 2023	Graph Neural NetworkLink Prediction	—Unverified
Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach	Jun 5, 2024	Mixture-of-ExpertsNode Classification	—Unverified
Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation	Apr 27, 2024	Node ClassificationRepresentation Learning	—Unverified
Non-Parametric Graph Learning for Bayesian Graph Neural Networks	Jun 23, 2020	Graph LearningLink Prediction	—Unverified
Non-Recursive Graph Convolutional Networks	May 9, 2021	Node ClassificationRepresentation Learning	—Unverified
NP^2L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks	Oct 2, 2023	Graph LearningLink Prediction	—Unverified
NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification	Jun 27, 2024	Node ClassificationRepresentation Learning	—Unverified
Octave Graph Convolutional Network	Sep 25, 2019	Node ClassificationRepresentation Learning	—Unverified
On Classification Thresholds for Graph Attention with Edge Features	Oct 18, 2022	ClassificationGraph Attention	—Unverified
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs	Nov 30, 2024	Link PredictionNode Classification	—Unverified
Hyperdimensional Representation Learning for Node Classification and Link Prediction	Feb 26, 2024	Classificationclass-incremental learning	—Unverified
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods	Jul 10, 2022	AttributeFairness	—Unverified
On Local Aggregation in Heterophilic Graphs	Jun 6, 2021	Node Classification	—Unverified
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks	Jun 8, 2023	Graph Neural NetworkNode Classification	—Unverified
On the Adversarial Robustness of Graph Contrastive Learning Methods	Nov 29, 2023	Adversarial RobustnessContrastive Learning	—Unverified
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks	Sep 26, 2024	Graph LearningGraph Neural Network	—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	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