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 551–575 of 1860 papers

Title	Date	Tasks	Status	Hype
Distilling Influences to Mitigate Prediction Churn in Graph Neural Networks	Oct 2, 2023	Knowledge DistillationNode Classification	CodeCode Available	0
NP^2L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks	Oct 2, 2023	Graph LearningLink Prediction	—Unverified	0
Sheaf Hypergraph Networks	Sep 29, 2023	Inductive BiasNode Classification	—Unverified	0
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?	Sep 28, 2023	Graph LearningNode Classification	CodeCode Available	1
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited	Sep 24, 2023	Node Classification	CodeCode Available	1
Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks	Sep 22, 2023	Node Classification	—Unverified	0
Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes	Sep 22, 2023	Node ClassificationNode Property Prediction	CodeCode Available	1
Article Classification with Graph Neural Networks and Multigraphs	Sep 20, 2023	ArticlesClassification	CodeCode Available	0
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks	Sep 19, 2023	CoLAContrastive Learning	CodeCode Available	1
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning	Sep 19, 2023	Graph LearningGraph Neural Network	—Unverified	0
Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy	Sep 14, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	0
Bregman Graph Neural Network	Sep 12, 2023	Bilevel OptimizationGraph Neural Network	CodeCode Available	0
Force-directed graph embedding with hops distance	Sep 11, 2023	Graph EmbeddingLink Prediction	CodeCode Available	0
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge Graphs	Sep 11, 2023	AttributeGraph Learning	CodeCode Available	0
Symplectic Structure-Aware Hamiltonian (Graph) Embeddings	Sep 9, 2023	Node ClassificationRiemannian optimization	—Unverified	0
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning	Sep 8, 2023	Graph ReconstructionGraph Representation Learning	—Unverified	0
Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing	Sep 6, 2023	Node ClassificationSelf-Supervised Learning	—Unverified	0
End-to-End Learning on Multimodal Knowledge Graphs	Sep 3, 2023	Knowledge GraphsLink Prediction	CodeCode Available	0
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative sampling	Sep 1, 2023	Contrastive LearningGraph Representation Learning	CodeCode Available	0
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark	Sep 1, 2023	Graph ClassificationGraph Learning	CodeCode Available	1
Domain-adaptive Message Passing Graph Neural Network	Aug 31, 2023	Domain AdaptationGRAPH DOMAIN ADAPTATION	CodeCode Available	0
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks	Aug 31, 2023	Node Classification	CodeCode Available	0
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks	Aug 31, 2023	DiversityLink Prediction	CodeCode Available	1
Over-Squashing in Graph Neural Networks: A Comprehensive survey	Aug 29, 2023	Link PredictionNode Classification	—Unverified	0
Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation	Aug 29, 2023	Graph Neural NetworkNode Classification	CodeCode Available	0

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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	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