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 951–975 of 1860 papers

Title	Date	Tasks	Status	Score
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions	May 24, 2021	Link PredictionNode Classification	CodeCode Available	5
Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs	Jun 7, 2021	Graph AttentionGraph Embedding	CodeCode Available	5
Generalized Equivariance and Preferential Labeling for GNN Node Classification	Feb 23, 2021	ClassificationGeneral Classification	CodeCode Available	5
POWN: Prototypical Open-World Node Classification	Jun 14, 2024	ClassificationData Augmentation	CodeCode Available	5
A Survey on Fairness for Machine Learning on Graphs	May 11, 2022	BIG-bench Machine LearningFairness	CodeCode Available	5
Predicting Properties of Nodes via Community-Aware Features	Nov 8, 2023	ClassificationNode Classification	CodeCode Available	5
LSCALE: Latent Space Clustering-Based Active Learning for Node Classification	Dec 13, 2020	Active LearningClustering	CodeCode Available	5
Predict then Propagate: Graph Neural Networks meet Personalized PageRank	Oct 14, 2018	General ClassificationNode Classification	CodeCode Available	5
Pre-train and Learn: Preserve Global Information for Graph Neural Networks	Oct 27, 2019	AttributeNode Classification	CodeCode Available	5
Adversarial network embedding with bootstrapped representations for sparse networks	Mar 3, 2025	Data VisualizationLink Prediction	CodeCode Available	5
Trajectory Encoding Temporal Graph Networks	Apr 15, 2025	Graph LearningLink Prediction	CodeCode Available	5
Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph	Sep 17, 2024	Node Classification	CodeCode Available	5
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction	Aug 7, 2019	Graph AttentionGraph Classification	CodeCode Available	5
Generative-Contrastive Heterogeneous Graph Neural Network	Apr 3, 2024	Contrastive LearningData Augmentation	CodeCode Available	5
Graph Neural Networks with convolutional ARMA filters	Jan 5, 2019	ClassificationGeneral Classification	CodeCode Available	5
Progressive Graph Convolutional Networks for Semi-Supervised Node Classification	Mar 27, 2020	ClassificationGeneral Classification	CodeCode Available	5
Connector 0.5: A unified framework for graph representation learning	Apr 25, 2023	Graph EmbeddingGraph Generation	CodeCode Available	5
Transfer Entropy in Graph Convolutional Neural Networks	Jun 8, 2024	Node Classification	CodeCode Available	5
Prototype-based Interpretable Graph Neural Networks	Nov 16, 2022	Explainable ModelsGraph Neural Network	CodeCode Available	5
Geometric deep learning on graphs and manifolds using mixture model CNNs	Nov 25, 2016	Deep LearningDocument Classification	CodeCode Available	5
DuoGNN: Topology-aware Graph Neural Network with Homophily and Heterophily Interaction-Decoupling	Sep 29, 2024	Graph Neural NetworkNode Classification	CodeCode Available	5
A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks	Jun 9, 2022	Link PredictionNode Classification	CodeCode Available	5
PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation	Dec 22, 2023	Continual LearningGraph Learning	CodeCode Available	5
PushNet: Efficient and Adaptive Neural Message Passing	Mar 4, 2020	Inductive BiasNode Classification	CodeCode Available	5
Geometric instability of graph neural networks on large graphs	Aug 19, 2023	Link PredictionNode Classification	CodeCode Available	5

Show:10 25 50

← PrevPage 39 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	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	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	Truncated Krylov	Accuracy	81.7	—	Unverified
5	SuperGAT MX	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