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 1451–1475 of 1860 papers

Title	Date	Tasks	Status
Hierarchical graph sampling based minibatch learning with chain preservation and variance reduction	Mar 2, 2025	Graph SamplingNode Classification	CodeCode Available
Graphite: Iterative Generative Modeling of Graphs	Mar 28, 2018	Density EstimationGeneral Classification	CodeCode Available
Addressing Heterophily in Node Classification with Graph Echo State Networks	May 14, 2023	Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs	CodeCode Available
Attributed Network Embedding for Incomplete Attributed Networks	Nov 28, 2018	AttributeLink Prediction	CodeCode Available
GraphGAN: Graph Representation Learning with Generative Adversarial Nets	Nov 22, 2017	Computational EfficiencyGraph Representation Learning	CodeCode Available
Progressive Graph Convolutional Networks for Semi-Supervised Node Classification	Mar 27, 2020	ClassificationGeneral Classification	CodeCode Available
Graph Fourier Transformer with Structure-Frequency Information	Apr 28, 2025	Graph ClassificationInductive Bias	CodeCode Available
Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks	Nov 7, 2024	Graph MiningNode Classification	CodeCode Available
NetEffect: Discovery and Exploitation of Generalized Network Effects	Dec 31, 2022	Graph MiningNode Classification	CodeCode Available
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs	Sep 26, 2019	AttributeClustering	CodeCode Available
GraphFM: A Comprehensive Benchmark for Graph Foundation Model	Jun 12, 2024	GPUGraph Neural Network	CodeCode Available
Prototype-based Interpretable Graph Neural Networks	Nov 16, 2022	Explainable ModelsGraph Neural Network	CodeCode Available
Unsupervised Graph Representation Learning with Inductive Shallow Node Embedding	Jul 12, 2024	Graph Representation LearningInductive Learning	CodeCode Available
High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approach	Nov 4, 2024	Anomaly DetectionGraph Anomaly Detection	CodeCode Available
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models	Aug 14, 2019	Graph Neural NetworkNode Classification	CodeCode Available
A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks	Jun 9, 2022	Link PredictionNode Classification	CodeCode Available
HoloNets: Spectral Convolutions do extend to Directed Graphs	Oct 3, 2023	Graph LearningNode Classification	CodeCode Available
PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation	Dec 22, 2023	Continual LearningGraph Learning	CodeCode Available
PushNet: Efficient and Adaptive Neural Message Passing	Mar 4, 2020	Inductive BiasNode Classification	CodeCode Available
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs	Sep 9, 2020	Knowledge GraphsLanguage Modelling	CodeCode Available
Adversarial network embedding with bootstrapped representations for sparse networks	Mar 3, 2025	Data VisualizationLink Prediction	CodeCode Available
Force-directed graph embedding with hops distance	Sep 11, 2023	Graph EmbeddingLink Prediction	CodeCode Available
Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution	Jan 11, 2022	Graph LearningGraph Neural Network	CodeCode Available
How Low Can You Go? Searching for the Intrinsic Dimensionality of Complex Networks using Metric Node Embeddings	Mar 3, 2025	Community DetectionLink Prediction	CodeCode Available
Deep Generative Models for Subgraph Prediction	Aug 7, 2024	Bayesian OptimizationGraph Learning	CodeCode Available

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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