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 926–950 of 1860 papers

Title	Date	Tasks	Status	Score
Partition-Based Active Learning for Graph Neural Networks	Jan 23, 2022	Active LearningNode Classification	CodeCode Available	5
Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening	May 20, 2025	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	5
Towards Sparse Hierarchical Graph Classifiers	Nov 3, 2018	ClassificationGeneral Classification	CodeCode Available	5
Kernel Node Embeddings	Sep 8, 2019	Link PredictionNode Classification	CodeCode Available	5
Towards Sparsification of Graph Neural Networks	Sep 11, 2022	image-classificationImage Classification	CodeCode Available	5
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees	May 7, 2022	Graph ClassificationNode Classification	CodeCode Available	5
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank	Jul 14, 2022	Graph EmbeddingGraph Learning	CodeCode Available	5
Independent Distribution Regularization for Private Graph Embedding	Aug 16, 2023	AttributeGraph Embedding	CodeCode Available	5
Edge Classification on Graphs: New Directions in Topological Imbalance	Jun 17, 2024	ClassificationEdge Classification	CodeCode Available	5
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction	Jan 28, 2024	Node Classification	CodeCode Available	5
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative sampling	Sep 1, 2023	Contrastive LearningGraph Representation Learning	CodeCode Available	5
Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks	Jun 8, 2024	AttributeData Augmentation	CodeCode Available	5
PGB: A PubMed Graph Benchmark for Heterogeneous Network Representation Learning	May 4, 2023	ArticlesGraph Mining	CodeCode Available	5
AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models	Aug 14, 2019	Graph Neural NetworkNode Classification	CodeCode Available	5
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions	Jun 14, 2021	Contrastive LearningGraph Learning	CodeCode Available	5
Generalized Laplacian Regularized Framelet Graph Neural Networks	Oct 27, 2022	DenoisingGraph Learning	CodeCode Available	5
Generalized Learning of Coefficients in Spectral Graph Convolutional Networks	Sep 7, 2024	Node Classification	CodeCode Available	5
Pitfalls of Graph Neural Network Evaluation	Nov 14, 2018	Graph MiningGraph Neural Network	CodeCode Available	5
Information Extraction from Visually Rich Documents Using Directed Weighted Graph Neural Network	Sep 11, 2024	Document Layout Analysisdocument understanding	CodeCode Available	5
Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network	Dec 3, 2023	Node Classification	CodeCode Available	5
Training a Label-Noise-Resistant GNN with Reduced Complexity	Nov 17, 2024	Graph Neural NetworkNode Classification	CodeCode Available	5
Policy-GNN: Aggregation Optimization for Graph Neural Networks	Jun 26, 2020	Deep Reinforcement LearningNode Classification	CodeCode Available	5
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation	Jan 18, 2024	Graph LearningNode Classification	CodeCode Available	5
Hard Label Black Box Node Injection Attack on Graph Neural Networks	Nov 22, 2023	ClassificationGraph Classification	CodeCode Available	5
Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups	Jan 10, 2024	Autism detectionClassification	CodeCode Available	5

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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