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 1751–1775 of 1860 papers

Title	Date	Tasks	Status
End-to-End Entity Classification on Multimodal Knowledge Graphs	Mar 25, 2020	ClassificationGeneral Classification	CodeCode Available
MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction	Apr 24, 2025	Link PredictionNode Classification	CodeCode Available
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding	Apr 6, 2021	Link PredictionNetwork Embedding	CodeCode Available
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification	Jun 28, 2024	ClassificationGraph Classification	CodeCode Available
Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy	Sep 14, 2023	Contrastive LearningDomain Adaptation	CodeCode Available
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective	Apr 10, 2019	ClassificationGeneral Classification	CodeCode Available
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning	Nov 5, 2023	Contrastive LearningGraph Classification	CodeCode Available
A Pure Transformer Pretraining Framework on Text-attributed Graphs	Jun 19, 2024	Link PredictionNode Classification	CodeCode Available
Endowing Pre-trained Graph Models with Provable Fairness	Feb 19, 2024	AttributeFairness	CodeCode Available
MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks	Nov 21, 2018	General ClassificationGraph Embedding	CodeCode Available
A Probabilistic Model for Node Classification in Directed Graphs	Jan 3, 2025	MathNode Classification	CodeCode Available
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data	Jun 27, 2018	Graph SamplingNode Classification	CodeCode Available
Multimodal weighted graph representation for information extraction from visually rich documents.	Jan 5, 2024	Document Layout Analysisdocument understanding	CodeCode Available
Multiple Kernel Representation Learning on Networks	Jun 9, 2021	Link PredictionNode Classification	CodeCode Available
Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities	Jun 1, 2021	Graph EmbeddingKnowledge Graph Embedding	CodeCode Available
Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach	Apr 15, 2024	Anomaly DetectionGraph Anomaly Detection	CodeCode Available
Semi-supervisedly Co-embedding Attributed Networks	Oct 31, 2019	AttributeNode Classification	CodeCode Available
Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network	Dec 23, 2023	Graph Neural NetworkNode Classification	CodeCode Available
Adversarial Attack on Network Embeddings via Supervised Network Poisoning	Feb 14, 2021	Adversarial AttackLink Prediction	CodeCode Available
Multitask Active Learning for Graph Anomaly Detection	Jan 24, 2024	Active LearningAnomaly Detection	CodeCode Available
Multi-Task Graph Autoencoders	Nov 7, 2018	General ClassificationGraph Embedding	CodeCode Available
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification	Feb 13, 2019	ClassificationGeneral Classification	CodeCode Available
Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks	Jun 8, 2024	AttributeData Augmentation	CodeCode Available
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge Graphs	Sep 11, 2023	AttributeGraph Learning	CodeCode Available
Efficient Mixed Precision Quantization in Graph Neural Networks	May 14, 2025	Graph ClassificationNode Classification	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	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