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 226–250 of 1860 papers

Title	Date	Tasks	Status	Hype
Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks	Apr 16, 2021	AttributeClustering	CodeCode Available	1
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling	Mar 21, 2022	Node Classification	CodeCode Available	1
CrossWalk: Fairness-enhanced Node Representation Learning	May 6, 2021	FairnessLink Prediction	CodeCode Available	1
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs	Mar 31, 2024	Hypergraph representationsNode Classification	CodeCode Available	1
Boosting Graph Embedding on a Single GPU	Oct 19, 2021	CPUGPU	CodeCode Available	1
A pipeline for fair comparison of graph neural networks in node classification tasks	Dec 19, 2020	Data AugmentationGeneral Classification	CodeCode Available	1
Boosting Multitask Learning on Graphs through Higher-Order Task Affinities	Jun 24, 2023	Community DetectionNode Classification	CodeCode Available	1
CSGCL: Community-Strength-Enhanced Graph Contrastive Learning	May 8, 2023	AttributeContrastive Learning	CodeCode Available	1
Hypergraph-MLP: Learning on Hypergraphs without Message Passing	Dec 15, 2023	Node ClassificationRepresentation Learning	CodeCode Available	1
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns	Jun 11, 2021	Graph LearningNode Classification	CodeCode Available	1
Imbalanced Graph Classification via Graph-of-Graph Neural Networks	Dec 1, 2021	ClassificationGraph Classification	CodeCode Available	1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEM	Jul 11, 2022	Graph Representation LearningNode Classification	CodeCode Available	1
I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on Hypergraphs	Jun 9, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Data Augmentation for Graph Neural Networks	Jun 11, 2020	Data AugmentationGeneral Classification	CodeCode Available	1
Decoupling the Depth and Scope of Graph Neural Networks	Jan 19, 2022	Link PredictionNode Classification	CodeCode Available	1
Lifelong Learning of Graph Neural Networks for Open-World Node Classification	Jun 25, 2020	Lifelong learningNode Classification	CodeCode Available	1
Inductive Entity Representations from Text via Link Prediction	Oct 7, 2020	Inductive knowledge graph completionInductive Link Prediction	CodeCode Available	1
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	Jun 16, 2021	Node ClassificationNode Property Prediction	CodeCode Available	1
Adversarial Deep Network Embedding for Cross-network Node Classification	Feb 18, 2020	ClassificationDomain Adaptation	CodeCode Available	1
Disentangled Condensation for Large-scale Graphs	Jan 18, 2024	Graph LearningLink Prediction	CodeCode Available	1
Jointly Learnable Data Augmentations for Self-Supervised GNNs	Aug 23, 2021	Data AugmentationGraph Representation Learning	CodeCode Available	1
A Representation Learning Framework for Property Graphs	Jun 27, 2022	Graph EmbeddingGraph Representation Learning	CodeCode Available	1
Adversarial Privacy Preserving Graph Embedding against Inference Attack	Aug 30, 2020	Graph EmbeddingInference Attack	CodeCode Available	1
Label Information Enhanced Fraud Detection against Low Homophily in Graphs	Feb 21, 2023	Fraud DetectionNode Classification	CodeCode Available	1
Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification	Oct 22, 2021	ClassificationGraph Neural Network	CodeCode Available	1

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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	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