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 776–800 of 1860 papers

Title	Date	Tasks	Status
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data	Jun 5, 2021	Causal InferenceGraph Learning	—Unverified
Graph Information Matters: Understanding Graph Filters from Interaction Probability	Sep 29, 2021	Graph LearningNode Classification	—Unverified
GraphiT: Efficient Node Classification on Text-Attributed Graphs with Prompt Optimized LLMs	Feb 14, 2025	Node Classification	—Unverified
Adaptive Data Augmentation on Temporal Graphs	Dec 1, 2021	Data AugmentationNode Classification	—Unverified
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks	Oct 23, 2023	Node Classification	—Unverified
DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs	May 20, 2022	Financial AnalysisMulti-Task Learning	—Unverified
Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching	Apr 19, 2024	ClassificationClustering	—Unverified
GENIE: Watermarking Graph Neural Networks for Link Prediction	Jun 7, 2024	Backdoor AttackDrug Discovery	—Unverified
Heterophily-Based Graph Neural Network for Imbalanced Classification	Oct 12, 2023	ClassificationGraph Neural Network	—Unverified
Attributed Network Embedding for Learning in a Dynamic Environment	Jun 6, 2017	AttributeClustering	—Unverified
Heterophily-Aware Graph Attention Network	Feb 7, 2023	Graph AttentionGraph Representation Learning	—Unverified
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning	Nov 11, 2024	Computational EfficiencyGraph Representation Learning	—Unverified
Graph Masked Language Models	Feb 24, 2025	Graph LearningKnowledge Graphs	—Unverified
Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks	Jul 27, 2024	MemorizationNode Classification	—Unverified
Graph Mining under Data scarcity	Jun 7, 2024	ClassificationFew-Shot Learning	—Unverified
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning	Oct 24, 2023	Contrastive LearningGraph Classification	—Unverified
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction	Jun 21, 2021	Node Classification	—Unverified
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection	Mar 15, 2024	Anomaly DetectionGraph Anomaly Detection	—Unverified
Convolutional Signal Propagation: A Simple Scalable Algorithm for Hypergraphs	Sep 26, 2024	Node ClassificationRetrieval	—Unverified
Graph Neural Aggregation-diffusion with Metastability	Mar 29, 2024	Node Classification	—Unverified
Generating Robust Counterfactual Witnesses for Graph Neural Networks	Apr 30, 2024	counterfactualExplanation Generation	—Unverified
Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness	Apr 24, 2023	Graph EmbeddingLink Prediction	—Unverified
Graph Neural Networks at a Fraction	Feb 10, 2025	Graph ClassificationLink Prediction	—Unverified
A Complex Network based Graph Embedding Method for Link Prediction	Sep 11, 2022	Data VisualizationGraph Embedding	—Unverified
Generating Human Understandable Explanations for Node Embeddings	Jun 11, 2024	Link PredictionNode 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	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	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