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 576–600 of 1860 papers

Title	Date	Tasks	Status	Score
Article Classification with Graph Neural Networks and Multigraphs	Sep 20, 2023	ArticlesClassification	CodeCode Available	5
Dimensionality Reduction Meets Message Passing for Graph Node Embeddings	Feb 1, 2022	Dimensionality ReductionNode Classification	CodeCode Available	5
Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization	Nov 10, 2023	Node ClassificationOut of Distribution (OOD) Detection	CodeCode Available	5
Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation	Jul 4, 2019	Graph ClassificationGraph Representation Learning	CodeCode Available	5
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds	Jun 15, 2023	Graph ReconstructionGraph Representation Learning	CodeCode Available	5
Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters	Jun 29, 2023	Node Classification	CodeCode Available	5
An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning	Nov 11, 2024	class-incremental learningClass Incremental Learning	CodeCode Available	5
Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers	Nov 1, 2022	FairnessImputation	CodeCode Available	5
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction	Jan 28, 2024	Node Classification	CodeCode Available	5
Diffusion-Convolutional Neural Networks	Nov 6, 2015	ClassificationGeneral Classification	CodeCode Available	5
An Attention-based Graph Neural Network for Heterogeneous Structural Learning	Dec 19, 2019	Graph EmbeddingGraph Neural Network	CodeCode Available	5
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning	Oct 8, 2024	Graph Representation LearningLink Prediction	CodeCode Available	5
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification	Jul 31, 2023	Active LearningGraph Learning	CodeCode Available	5
IGCN: Integrative Graph Convolution Networks for patient level insights and biomarker discovery in multi-omics integration	Jan 31, 2024	Node Classification	CodeCode Available	5
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank	Jul 14, 2022	Graph EmbeddingGraph Learning	CodeCode Available	5
DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations	Jan 28, 2024	AttributeGraph Embedding	CodeCode Available	5
Hypergraph Neural Networks for Hypergraph Matching	Jan 1, 2021	Graph MatchingHypergraph Matching	CodeCode Available	5
DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters	Oct 24, 2019	Document ClassificationGeneral Classification	CodeCode Available	5
DFG-NAS: Deep and Flexible Graph Neural Architecture Search	Jun 17, 2022	Neural Architecture SearchNode Classification	CodeCode Available	5
Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs	Sep 26, 2024	BenchmarkingConformal Prediction	CodeCode Available	5
BeMap: Balanced Message Passing for Fair Graph Neural Network	Jun 7, 2023	FairnessGraph Neural Network	CodeCode Available	5
Bayesian Robust Graph Contrastive Learning	May 27, 2022	Contrastive LearningNode Classification	CodeCode Available	5
Hyperedge Anomaly Detection with Hypergraph Neural Network	Dec 7, 2024	Anomaly DetectionLink Prediction	CodeCode Available	5
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks	Jun 22, 2022	Bilevel OptimizationClassification	CodeCode Available	5
Derivation of Back-propagation for Graph Convolutional Networks using Matrix Calculus and its Application to Explainable Artificial Intelligence	Aug 2, 2024	Explainable artificial intelligenceLink Prediction	CodeCode Available	5

Show:10 25 50

← PrevPage 24 of 75Next →

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	TransGNN	1:1 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	GEM	Accuracy	74.2	—	Unverified
6	GGCM	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