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 1826–1850 of 1860 papers

Title	Date	Tasks	Status
Unsupervised Adversarially-Robust Representation Learning on Graphs	Dec 4, 2020	Adversarial RobustnessCommunity Detection	—Unverified
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees	Mar 4, 2025	Link PredictionNetwork Embedding	—Unverified
Unsupervised Domain-adaptive Hash for Networks	Aug 20, 2021	Link PredictionNode Classification	—Unverified
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models	Oct 8, 2020	Graph Neural NetworkNode Classification	—Unverified
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models	Dec 1, 2020	Graph Neural NetworkNode Classification	—Unverified
Unsupervised Learning of Node Embeddings by Detecting Communities	Sep 25, 2019	ClusteringNode Classification	—Unverified
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information	Oct 7, 2022	Contrastive LearningGraph Attention	—Unverified
Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs	Aug 25, 2023	Node ClassificationPrivacy Preserving	—Unverified
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs	Nov 20, 2023	Graph LearningGraph Neural Network	—Unverified
Using Graph Algorithms to Pretrain Graph Completion Transformers	Oct 14, 2022	Graph EmbeddingKnowledge Graph Completion	—Unverified
Using Subgraph GNNs for Node Classification:an Overlooked Potential Approach	Mar 9, 2025	ClassificationNode Classification	—Unverified
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification	Mar 26, 2024	Graph Representation LearningInductive Learning	—Unverified
VECoDeR - Variational Embeddings for Community Detection and Node Representation	Jan 1, 2021	Community DetectionNode Classification	—Unverified
VEM-GCN: Topology Optimization with Variational EM for Graph Convolutional Networks	Jan 1, 2021	ClassificationGeneral Classification	—Unverified
VersaGNN: a Versatile accelerator for Graph neural networks	May 4, 2021	CPUGPU	—Unverified
Vertex-Centric Visual Programming for Graph Neural Networks	Jun 9, 2021	Graph ClusteringLink Prediction	—Unverified
VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning	Feb 24, 2025	Adversarial AttackContrastive Learning	—Unverified
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification	Nov 2, 2023	Contrastive LearningNode Classification	—Unverified
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification	Feb 28, 2019	ClassificationGeneral Classification	—Unverified
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs	Sep 12, 2024	Graph LearningMeta-Learning	—Unverified
Virtual Node Tuning for Few-shot Node Classification	Jun 9, 2023	ClassificationGraph Representation Learning	—Unverified
Wasserstein Graph Neural Networks for Graphs with Missing Attributes	Feb 6, 2021	AttributeGraph Neural Network	—Unverified
Wasserstein Hypergraph Neural Network	Jun 11, 2025	Graph Representation LearningNode Classification	—Unverified
Watermarking Graph Neural Networks based on Backdoor Attacks	Oct 21, 2021	ClassificationGraph Classification	—Unverified
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification	Jun 12, 2021	ClassificationDrug Design	—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	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