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
Inductive Graph Embeddings through Locality Encodings	Sep 26, 2020	Language ModelingLanguage Modelling	—Unverified
Inductive Graph Few-shot Class Incremental Learning	Nov 11, 2024	class-incremental learningClass Incremental Learning	—Unverified
Inductive Linear Probing for Few-shot Node Classification	Jun 14, 2023	ClassificationFew-Shot Learning	—Unverified
Inductive Lottery Ticket Learning for Graph Neural Networks	Sep 29, 2021	Graph ClassificationNode Classification	—Unverified
Infant Cry Classification with Graph Convolutional Networks	Jan 31, 2021	ClassificationDiversity	—Unverified
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs	Jun 24, 2024	Graph Representation LearningInductive Bias	—Unverified
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels	Jan 20, 2022	InformativenessNode Classification	—Unverified
Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning	May 15, 2025	Contrastive LearningGraph Learning	—Unverified
InstantEmbedding: Efficient Local Node Representations	Oct 14, 2020	Link PredictionNode Classification	—Unverified
Mixed Graph Contrastive Network for Semi-Supervised Node Classification	Jun 6, 2022	ClassificationContrastive Learning	—Unverified
Interpreting and Understanding Graph Convolutional Neural Network using Gradient-based Attribution Method	Mar 9, 2019	Few-Shot LearningNode Classification	—Unverified
Investigating Extensions to Random Walk Based Graph Embedding	Feb 17, 2020	Graph EmbeddingLink Prediction	—Unverified
Investigating Transfer Learning in Graph Neural Networks	Feb 1, 2022	ClassificationGraph Classification	—Unverified
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?	Sep 12, 2021	Inductive BiasNode Classification	—Unverified
Is Heterophily A Real Nightmare For Graph Neural Networks on Performing Node Classification?	Sep 29, 2021	Inductive BiasNode Classification	—Unverified
Is Heterophily A Real Nightmare For Graph Neural Networks Performing Node Classification?	May 21, 2021	Inductive BiasNode Classification	—Unverified
Is Homophily a Necessity for Graph Neural Networks?	Jun 11, 2021	Node Classification	—Unverified
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning	Dec 9, 2023	Graph ClassificationGraph Learning	—Unverified
Iterated graph neural network system	Jan 1, 2021	Graph Neural NetworkNode Classification	—Unverified
Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations	Mar 12, 2024	Graph LearningGraph Neural Network	—Unverified
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms	Jan 16, 2021	AutoMLLink Prediction	—Unverified
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach	Jan 22, 2022	Graph Representation LearningLink Prediction	—Unverified
Joint Use of Node Attributes and Proximity for Semi-Supervised Classification on Graphs	Oct 22, 2020	General ClassificationNode Classification	—Unverified
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks	Oct 12, 2022	Active LearningGraph Mining	—Unverified
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks	Apr 9, 2019	General ClassificationNode 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	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