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 1101–1125 of 1860 papers

Title	Date	Tasks	Status
Pair-view Unsupervised Graph Representation Learning	Dec 11, 2020	Graph Representation LearningLink Prediction	—Unverified
PanRep: Universal node embeddings for heterogeneous graphs	Sep 28, 2020	Graph Neural NetworkLink Prediction	—Unverified
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis	May 19, 2022	Graph ClassificationLink Prediction	—Unverified
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation	Aug 10, 2024	Graph LearningGraph Representation Learning	—Unverified
Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics	May 19, 2025	Domain AdaptationGRAPH DOMAIN ADAPTATION	—Unverified
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks	Mar 3, 2022	Community DetectionLink Prediction	—Unverified
Permutohedral-GCN: Graph Convolutional Networks with Global Attention	Mar 2, 2020	Node Classification	—Unverified
Personalized Layer Selection for Graph Neural Networks	Jan 24, 2025	Metric LearningNode Classification	—Unverified
PersonaSAGE: A Multi-Persona Graph Neural Network	Dec 28, 2022	Graph Neural NetworkLink Prediction	—Unverified
Perturbation Ontology based Graph Attention Networks	Nov 27, 2024	Graph AttentionGraph Representation Learning	—Unverified
PI-GNN: Towards Robust Semi-Supervised Node Classification against Noisy Labels	Sep 29, 2021	Graph MiningGraph Neural Network	—Unverified
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions	Sep 25, 2019	Community DetectionGraph Embedding	—Unverified
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding	Dec 19, 2023	Graph EmbeddingInductive Bias	—Unverified
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks	Jun 25, 2023	graph partitioningNode Classification	—Unverified
Positive-Unlabeled Node Classification with Structure-aware Graph Learning	Oct 20, 2023	Anomaly DetectionClassification	—Unverified
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields	Nov 8, 2024	Graph Representation LearningNode Classification	—Unverified
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning	Jan 14, 2025	Abstention PredictionDynamic Link Prediction	—Unverified
Predicting the structure of dynamic graphs	Jan 8, 2024	Incremental LearningLink Prediction	—Unverified
Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs	Jul 22, 2024	Few-Shot LearningGraph Neural Network	—Unverified
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification	May 25, 2020	ClassificationGeneral Classification	—Unverified
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective	Jul 3, 2021	Link PredictionNode Classification	—Unverified
Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs	Nov 29, 2023	Graph Neural NetworkKnowledge Distillation	—Unverified
Propagation with Adaptive Mask then Training for Node Classification on Attributed Networks	Jun 21, 2022	AttributeNode Classification	—Unverified
Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks	Sep 22, 2023	Node Classification	—Unverified
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More	Dec 5, 2023	Adversarial RobustnessMolecular Property Prediction	—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	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	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