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 1251–1275 of 1860 papers

Title	Date	Tasks	Status
Exploring Graph Neural Networks for Indian Legal Judgment Prediction	Oct 19, 2023	FairnessGraph Neural Network	—Unverified
Pre-Trained Models for Heterogeneous Information Networks	Jul 7, 2020	ClusteringLink Prediction	—Unverified
Exploring the Potential of Large Language Models in Graph Generation	Mar 21, 2024	Drug DiscoveryGraph Generation	—Unverified
Exploring the Potential of Large Language Models for Heterophilic Graphs	Aug 26, 2024	Node ClassificationWorld Knowledge	—Unverified
Exponential Family Graph Embeddings	Nov 20, 2019	Graph EmbeddingGraph Learning	—Unverified
Extracting Shopping Interest-Related Product Types from the Web	May 23, 2023	Node Classification	—Unverified
FairGAT: Fairness-aware Graph Attention Networks	Mar 26, 2023	FairnessGraph Attention	—Unverified
Fair Graph Neural Network with Supervised Contrastive Regularization	Apr 9, 2024	counterfactualFairness	—Unverified
Fairness-Aware Graph Filter Design	Mar 20, 2023	Decision MakingFairness	—Unverified
Fairness-Aware Node Representation Learning	Jun 9, 2021	Contrastive LearningFairness	—Unverified
Fairness-aware Optimal Graph Filter Design	Oct 22, 2023	Decision MakingFairness	—Unverified
Fair Node Representation Learning via Adaptive Data Augmentation	Jan 21, 2022	Contrastive LearningData Augmentation	—Unverified
FairNorm: Fair and Fast Graph Neural Network Training	May 20, 2022	FairnessGraph Neural Network	—Unverified
FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification	Jan 1, 2021	ClassificationGeneral Classification	—Unverified
Fast and Effective GNN Training with Linearized Random Spanning Trees	Jun 7, 2023	Node Classification	—Unverified
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation	May 2, 2022	ClassificationContrastive Learning	—Unverified
Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification	Jun 15, 2020	Graph AttentionNode Classification	—Unverified
Fast Node Embeddings: Learning Ego-Centric Representations	Jan 1, 2018	Link PredictionMachine Translation	—Unverified
Fast online node labeling with graph subsampling	Mar 21, 2025	Computational EfficiencyNode Classification	—Unverified
FEATURE-AUGMENTED HYPERGRAPH NEURAL NETWORKS	Sep 29, 2021	Node ClassificationRepresentation Learning	—Unverified
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective	Feb 7, 2024	Node Classification	—Unverified
Federated Learning over Coupled Graphs	Jan 26, 2023	Federated LearningNode Classification	—Unverified
Federated Learning with Limited Node Labels	Jun 18, 2024	Federated LearningNode Classification	—Unverified
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks	Dec 20, 2024	Graph AttentionGraph Learning	—Unverified
FedGT: Federated Node Classification with Scalable Graph Transformer	Jan 26, 2024	ClassificationFederated Learning	—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