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 851–875 of 1860 papers

Title	Date	Tasks	Status
Graph Transformer GANs for Graph-Constrained House Generation	Mar 14, 2023	Generative Adversarial NetworkHouse Generation	—Unverified
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation	Jan 15, 2024	Generative Adversarial NetworkGraph Representation Learning	—Unverified
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels	Jan 20, 2022	InformativenessNode Classification	—Unverified
Latent Network Embedding via Adversarial Auto-encoders	Sep 30, 2021	Link PredictionNetwork Embedding	—Unverified
GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs	Nov 3, 2020	Graph AttentionGraph Neural Network	—Unverified
Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE	Jun 3, 2024	Graph Neural NetworkImage-text Classification	—Unverified
GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck	Nov 7, 2024	Graph structure learningNode Classification	—Unverified
GAGE: Geometry Preserving Attributed Graph Embeddings	Nov 3, 2020	AttributeLink Prediction	—Unverified
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs	Sep 27, 2021	Dynamic Node ClassificationLink Prediction	—Unverified
Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training	Nov 27, 2023	Node Classification	—Unverified
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation	Nov 9, 2023	Node Classification	—Unverified
Graph View-Consistent Learning Network	Jan 1, 2021	Node ClassificationPseudo Label	—Unverified
Constant Curvature Graph Convolutional Networks	Nov 12, 2019	Node Classification	—Unverified
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning	May 24, 2025	Link PredictionNode Classification	—Unverified
A Collective Learning Framework to Boost GNN Expressiveness	Mar 26, 2020	ClassificationGeneral Classification	—Unverified
Attacking Graph Convolutional Networks via Rewiring	Jun 10, 2019	General ClassificationGraph Classification	—Unverified
Adapt, Agree, Aggregate: Semi-Supervised Ensemble Labeling for Graph Convolutional Networks	Mar 22, 2025	Ensemble LearningNode Classification	—Unverified
Hyperedge Modeling in Hypergraph Neural Networks by using Densest Overlapping Subgraphs	Sep 16, 2024	Graph ClusteringNode Classification	—Unverified
Disentangled Hyperbolic Representation Learning for Heterogeneous Graphs	Jun 14, 2024	Link PredictionNode Classification	—Unverified
Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks	Dec 11, 2024	Adversarial RobustnessClassification	—Unverified
HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views	Feb 18, 2025	AttributeContrastive Learning	—Unverified
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation	Jul 1, 2020	Node Classification	—Unverified
G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning	Nov 3, 2024	Graph LearningLink Prediction	—Unverified
From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks	Jul 12, 2022	Node Classification	—Unverified
Connecting Graph Convolution and Graph PCA	Sep 29, 2021	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	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