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

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Showing 1151–1175 of 1860 papers

Title	Date	Tasks	Status
Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective	Sep 24, 2020	General ClassificationNode Classification	—Unverified
Revisiting Graph Neural Networks for Link Prediction	Sep 28, 2020	ClassificationGraph Classification	—Unverified
Revisiting Heterophily in Graph Convolution Networks by Learning Representations Across Topological and Feature Spaces	Nov 1, 2022	Node Classification	—Unverified
Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing	Jul 21, 2024	ClassificationNode Classification	—Unverified
Revisiting Over-smoothing in Deep GCNs	Mar 30, 2020	Node Classification	—Unverified
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning	Jul 1, 2022	Few-Shot LearningLink Prediction	—Unverified
RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection	Jun 18, 2021	Fraud DetectionGraph Neural Network	—Unverified
NodeSig: Binary Node Embeddings via Random Walk Diffusion	Oct 1, 2020	Graph Representation LearningLink Prediction	—Unverified
Robustness Inspired Graph Backdoor Defense	Jun 14, 2024	backdoor defenseGraph Classification	—Unverified
Robust Subgraph Learning by Monitoring Early Training Representations	Mar 14, 2024	Adversarial RobustnessDecision Making	—Unverified
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification	Aug 25, 2024	Conformal PredictionLink Prediction	—Unverified
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning	Feb 28, 2024	DenoisingGraph Learning	—Unverified
RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling	Aug 13, 2024	Anomaly DetectionClassification	—Unverified
SA-GDA: Spectral Augmentation for Graph Domain Adaptation	Aug 17, 2024	Domain AdaptationGRAPH DOMAIN ADAPTATION	—Unverified
SAS: A Simple, Accurate and Scalable Node Classification Algorithm	Apr 19, 2021	ClassificationGeneral Classification	—Unverified
Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling	May 2, 2025	Graph LearningLink Prediction	—Unverified
Scalable Deep Metric Learning on Attributed Graphs	Nov 20, 2024	Contrastive LearningGraph Embedding	—Unverified
SearchGCN: Powering Embedding Retrieval by Graph Convolution Networks for E-Commerce Search	Jul 1, 2021	Node ClassificationRetrieval	—Unverified
Seastar: vertex-centric programming for graph neural networks	Apr 21, 2021	GPUGraph Clustering	—Unverified
SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods	Jun 16, 2021	Node Classification	—Unverified
Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks	May 8, 2022	Node Classification	—Unverified
Self-attention Dual Embedding for Graphs with Heterophily	May 28, 2023	InformativenessNode Classification	—Unverified
Self-Directed Learning of Convex Labelings on Graphs	Sep 2, 2024	Node Classification	—Unverified
Self-Explainable Graph Neural Networks for Link Prediction	May 21, 2023	Link PredictionNode Classification	—Unverified
Self-Supervised Graph Representation Learning via Global Context Prediction	Mar 3, 2020	ClusteringGraph Representation Learning	—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	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	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	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