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 51–75 of 1860 papers

Title	Date	Tasks	Status	Hype
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?	Feb 22, 2023	Graph Representation LearningNode Classification	CodeCode Available	1
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods	Nov 8, 2021	Node Classification	CodeCode Available	1
CLNode: Curriculum Learning for Node Classification	Jun 15, 2022	ClassificationNode Classification	CodeCode Available	1
A Meta-Learning Approach for Training Explainable Graph Neural Networks	Sep 20, 2021	Graph ClassificationMeta-Learning	CodeCode Available	1
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks	May 20, 2019	ClusteringComputational Efficiency	CodeCode Available	1
A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs	Nov 11, 2022	Graph LearningGraph Neural Network	CodeCode Available	1
An Empirical Study of Graph Contrastive Learning	Sep 2, 2021	Graph ClassificationManagement	CodeCode Available	1
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs	Mar 10, 2020	BIG-bench Machine LearningClustering	CodeCode Available	1
Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification	Nov 26, 2024	ClassificationGraph Mining	CodeCode Available	1
Adversarial Deep Network Embedding for Cross-network Node Classification	Feb 18, 2020	ClassificationDomain Adaptation	CodeCode Available	1
CSGCL: Community-Strength-Enhanced Graph Contrastive Learning	May 8, 2023	AttributeContrastive Learning	CodeCode Available	1
Adversarial Immunization for Certifiable Robustness on Graphs	Jul 19, 2020	Adversarial AttackBilevel Optimization	CodeCode Available	1
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
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking	Oct 14, 2022	BenchmarkingGPU	CodeCode Available	1
A Deep Graph Wavelet Convolutional Neural Network for Semi-supervised Node Classification	Feb 19, 2021	General ClassificationNode Classification	CodeCode Available	1
Class Label-aware Graph Anomaly Detection	Aug 22, 2023	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	1
A pipeline for fair comparison of graph neural networks in node classification tasks	Dec 19, 2020	Data AugmentationGeneral Classification	CodeCode Available	1
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability	Jun 13, 2024	Graph ClassificationGraph Regression	CodeCode Available	1
Cleora: A Simple, Strong and Scalable Graph Embedding Scheme	Feb 3, 2021	Contrastive LearningCPU	CodeCode Available	1
Composition-based Multi-Relational Graph Convolutional Networks	Nov 8, 2019	General ClassificationGraph Classification	CodeCode Available	1
Deep Graph Contrastive Representation Learning	Jun 7, 2020	AttributeContrastive Learning	CodeCode Available	1
Can GNN be Good Adapter for LLMs?	Feb 20, 2024	Graph Neural NetworkNode Classification	CodeCode Available	1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks	Mar 26, 2020	Graph ClassificationGraph Learning	CodeCode Available	1
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?	Sep 28, 2023	Graph LearningNode Classification	CodeCode Available	1
A data-centric approach for assessing progress of Graph Neural Networks	Jun 18, 2024	ClassificationMulti-class Classification	CodeCode Available	1

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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