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 451–475 of 1860 papers

Title	Date	Tasks	Status	Hype
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation	Jan 18, 2024	Graph LearningNode Classification	CodeCode Available	0
Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering	Jan 17, 2024	Node Classification	—Unverified	0
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation	Jan 15, 2024	Generative Adversarial NetworkGraph Representation Learning	—Unverified	0
Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups	Jan 10, 2024	Autism detectionClassification	CodeCode Available	0
Predicting the structure of dynamic graphs	Jan 8, 2024	Incremental LearningLink Prediction	—Unverified	0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation	Jan 7, 2024	Graph LearningInterpretable Machine Learning	—Unverified	0
Multimodal weighted graph representation for information extraction from visually rich documents.	Jan 5, 2024	Document Layout Analysisdocument understanding	CodeCode Available	0
Effective backdoor attack on graph neural networks in link prediction tasks	Jan 5, 2024	Backdoor AttackGraph Classification	—Unverified	0
Strong Transitivity Relations and Graph Neural Networks	Jan 1, 2024	Graph Neural NetworkNode Classification	CodeCode Available	0
A clean-label graph backdoor attack method in node classification task	Dec 30, 2023	Backdoor AttackNode Classification	—Unverified	0
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation	Dec 28, 2023	Adversarial AttackNode Classification	—Unverified	0
Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures	Dec 28, 2023	Node ClassificationNode Clustering	CodeCode Available	1
Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding	Dec 28, 2023	Graph EmbeddingLink Prediction	CodeCode Available	0
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks	Dec 27, 2023	Contrastive LearningNode Classification	—Unverified	0
RDF-star2Vec: RDF-star Graph Embeddings for Data Mining	Dec 25, 2023	BenchmarkingGraph Embedding	CodeCode Available	0
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training	Dec 24, 2023	Graph Neural NetworkLink Prediction	CodeCode Available	0
Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network	Dec 23, 2023	Graph Neural NetworkNode Classification	CodeCode Available	0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation	Dec 22, 2023	Continual LearningGraph Learning	CodeCode Available	0
PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering	Dec 22, 2023	Graph Representation LearningNode Classification	CodeCode Available	1
DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)	Dec 21, 2023	Node Classification	CodeCode Available	0
NodeMixup: Tackling Under-Reaching for Graph Neural Networks	Dec 20, 2023	Node Classification	CodeCode Available	0
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding	Dec 19, 2023	Graph EmbeddingInductive Bias	—Unverified	0
Chasing Fairness in Graphs: A GNN Architecture Perspective	Dec 19, 2023	AttributeContrastive Learning	CodeCode Available	0
Graph Transformers for Large Graphs	Dec 18, 2023	Graph LearningGraph Property Prediction	CodeCode Available	1
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity	Dec 18, 2023	Active LearningDiversity	—Unverified	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	NodeNet	Accuracy	86.8	—	Unverified
7	Cleora	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