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 1051–1075 of 1860 papers

Title	Date	Tasks	Status
Framelet Message Passing	Feb 28, 2023	Node Classification	—Unverified
Scalable Neural Network Training over Distributed Graphs	Feb 25, 2023	Node Classification	CodeCode Available
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification	Feb 24, 2023	ClassificationData Augmentation	—Unverified
Random Projection Forest Initialization for Graph Convolutional Networks	Feb 22, 2023	graph constructionGraph Embedding	CodeCode Available
Graph Construction using Principal Axis Trees for Simple Graph Convolution	Feb 22, 2023	graph constructionGraph Embedding	CodeCode Available
Diffusion Probabilistic Models for Structured Node Classification	Feb 21, 2023	ClassificationNode Classification	—Unverified
Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification	Feb 20, 2023	Node Classification	—Unverified
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks	Feb 17, 2023	Contrastive LearningNode Classification	—Unverified
On Generalized Degree Fairness in Graph Neural Networks	Feb 8, 2023	FairnessGraph Neural Network	CodeCode Available
Heterophily-Aware Graph Attention Network	Feb 7, 2023	Graph AttentionGraph Representation Learning	—Unverified
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks	Feb 6, 2023	Adversarial RobustnessImage Segmentation	—Unverified
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search	Feb 6, 2023	Decision MakingGraph Classification	—Unverified
Simple yet Effective Gradient-Free Graph Convolutional Networks	Feb 1, 2023	Graph Neural NetworkGraph Representation Learning	—Unverified
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization	Jan 31, 2023	Node Classification	—Unverified
KG-Hub -- Building and Exchanging Biological Knowledge Graphs	Jan 31, 2023	Knowledge GraphsLink Prediction	—Unverified
Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning	Jan 31, 2023	Contrastive LearningNode Classification	CodeCode Available
Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification	Jan 31, 2023	ClassificationNode Classification	CodeCode Available
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention	Jan 27, 2023	ClassificationFederated Learning	—Unverified
Federated Learning over Coupled Graphs	Jan 26, 2023	Federated LearningNode Classification	—Unverified
Graph Neural Tangent Kernel: Convergence on Large Graphs	Jan 25, 2023	Node ClassificationNode Regression	—Unverified
Feature Selection: Key to Enhance Node Classification with Graph Neural Networks	Jan 25, 2023	feature selectionInductive Bias	CodeCode Available
Neighborhood Homophily-based Graph Convolutional Network	Jan 24, 2023	Node Classification	CodeCode Available
Determinate Node Selection for Semi-supervised Classification Oriented Graph Convolutional Networks	Jan 11, 2023	Graph Neural NetworkNode Classification	—Unverified
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer	Jan 7, 2023	ClassificationDiagnostic	—Unverified
Few-shot Node Classification with Extremely Weak Supervision	Jan 6, 2023	ClassificationMeta-Learning	CodeCode Available

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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	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