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 276–300 of 1860 papers

Title	Date	Tasks	Status	Hype	Score
CrossWalk: Fairness-enhanced Node Representation Learning	May 6, 2021	FairnessLink Prediction	CodeCode Available	1	5
CKGConv: General Graph Convolution with Continuous Kernels	Apr 21, 2024	Graph ClassificationGraph Learning	CodeCode Available	1	5
Generative Subgraph Contrast for Self-Supervised Graph Representation Learning	Jul 25, 2022	Contrastive LearningGraph Representation Learning	CodeCode Available	1	5
Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited	Feb 4, 2022	GPRGraph Learning	CodeCode Available	1	5
Confidence-Based Feature Imputation for Graphs with Partially Known Features	May 26, 2023	Graph LearningImputation	CodeCode Available	1	5
GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks	Aug 14, 2022	Few-Shot LearningNode Classification	CodeCode Available	1	5
Class Label-aware Graph Anomaly Detection	Aug 22, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	1	5
GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment	Apr 4, 2022	Node ClassificationSelf-Supervised Learning	CodeCode Available	1	5
Cleora: A Simple, Strong and Scalable Graph Embedding Scheme	Feb 3, 2021	Contrastive LearningCPU	CodeCode Available	1	5
A Survey of Adversarial Learning on Graphs	Mar 10, 2020	ClusteringGraph Clustering	CodeCode Available	1	5
CLNode: Curriculum Learning for Node Classification	Jun 15, 2022	ClassificationNode Classification	CodeCode Available	1	5
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks	Jan 28, 2022	Federated LearningNode Classification	CodeCode Available	1	5
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions	Aug 26, 2023	Graph LearningLink Prediction	CodeCode Available	1	5
Robust Optimization as Data Augmentation for Large-scale Graphs	Oct 19, 2020	Data AugmentationGraph Classification	CodeCode Available	1	5
Deep Learning for Abstract Argumentation Semantics	Jul 15, 2020	Abstract ArgumentationDeep Learning	CodeCode Available	1	5
Gated Graph Sequence Neural Networks	Nov 17, 2015	Drug DiscoveryGraph Classification	CodeCode Available	1	5
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods	Nov 8, 2021	Node Classification	CodeCode Available	1	5
Graph Coloring with Physics-Inspired Graph Neural Networks	Feb 3, 2022	ClusteringCommunity Detection	CodeCode Available	1	5
Graph Contrastive Invariant Learning from the Causal Perspective	Jan 23, 2024	Contrastive LearningNode Classification	CodeCode Available	1	5
Graph Convolutional Networks for Road Networks	Aug 30, 2019	AttributeBIG-bench Machine Learning	CodeCode Available	1	5
Fast Graph Learning with Unique Optimal Solutions	Feb 17, 2021	FormGraph Learning	CodeCode Available	1	5
Combining Label Propagation and Simple Models Out-performs Graph Neural Networks	Oct 27, 2020	Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs	CodeCode Available	1	5
Deep Graph Contrastive Representation Learning	Jun 7, 2020	AttributeContrastive Learning	CodeCode Available	1	5
A Survey on Role-Oriented Network Embedding	Jul 18, 2021	ClusteringGraph Mining	CodeCode Available	1	5
A Self-Attention Network based Node Embedding Model	Jun 22, 2020	General ClassificationLink Prediction	CodeCode Available	1	5

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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