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 951–975 of 1860 papers

Title	Date	Tasks	Status
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning	Aug 17, 2023	Active LearningDiversity	CodeCode Available
Independent Distribution Regularization for Private Graph Embedding	Aug 16, 2023	AttributeGraph Embedding	CodeCode Available
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification	Aug 15, 2023	Node ClassificationRepresentation Learning	—Unverified
Node Embedding for Homophilous Graphs with ARGEW: Augmentation of Random walks by Graph Edge Weights	Aug 11, 2023	Node Classification	—Unverified
G^2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns	Aug 10, 2023	ClassificationInductive Learning	—Unverified
Local Structure-aware Graph Contrastive Representation Learning	Aug 7, 2023	Contrastive LearningGraph Neural Network	—Unverified
Communication-Free Distributed GNN Training with Vertex Cut	Aug 6, 2023	GPUNode Classification	—Unverified
Label Inference Attacks against Node-level Vertical Federated GNNs	Aug 4, 2023	Federated LearningNode Classification	—Unverified
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification	Jul 31, 2023	Active LearningGraph Learning	CodeCode Available
Feature Transportation Improves Graph Neural Networks	Jul 29, 2023	Node Classification	—Unverified
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs	Jul 29, 2023	Contrastive LearningDiversity	—Unverified
Addressing the Impact of Localized Training Data in Graph Neural Networks	Jul 24, 2023	Anomaly DetectionGraph Generation	CodeCode Available
Learning Adaptive Neighborhoods for Graph Neural Networks	Jul 18, 2023	Node ClassificationPoint Cloud Classification	—Unverified
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs	Jul 17, 2023	Graph Representation LearningNode Classification	—Unverified
Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach	Jul 17, 2023	Link PredictionNode Classification	—Unverified
Automated Knowledge Modeling for Cancer Clinical Practice Guidelines	Jul 15, 2023	ClassificationNode Classification	—Unverified
Supervised Attention Using Homophily in Graph Neural Networks	Jul 11, 2023	Graph AttentionNode Classification	—Unverified
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs	Jul 7, 2023	Graph LearningGraph Neural Network	CodeCode Available
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers	Jul 5, 2023	Anomaly DetectionComputational Efficiency	CodeCode Available
HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks	Jul 4, 2023	Link PredictionNode Classification	—Unverified
A Survey on Graph Classification and Link Prediction based on GNN	Jul 3, 2023	Graph ClassificationGraph Neural Network	—Unverified
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals	Jul 1, 2023	DenoisingGenerative Adversarial Network	CodeCode Available
Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters	Jun 29, 2023	Node Classification	CodeCode Available
Contrastive Meta-Learning for Few-shot Node Classification	Jun 27, 2023	ClassificationGraph Mining	CodeCode Available
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks	Jun 25, 2023	graph partitioningNode 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	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