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 501–525 of 1860 papers

Title	Date	Tasks	Status	Hype
Node Classification in Random Trees	Nov 20, 2023	ClassificationGraph Neural Network	CodeCode Available	0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs	Nov 20, 2023	Graph LearningGraph Neural Network	—Unverified	0
Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks	Nov 19, 2023	Link PredictionNode Classification	—Unverified	0
Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization	Nov 10, 2023	Node ClassificationOut of Distribution (OOD) Detection	CodeCode Available	0
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation	Nov 9, 2023	Node Classification	—Unverified	0
Mixture of Weak & Strong Experts on Graphs	Nov 9, 2023	Graph Neural NetworkNode Classification	CodeCode Available	1
Predicting Properties of Nodes via Community-Aware Features	Nov 8, 2023	ClassificationNode Classification	CodeCode Available	0
Edge2Node: Reducing Edge Prediction to Node Classification	Nov 6, 2023	Graph Neural NetworkLink Prediction	—Unverified	0
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning	Nov 5, 2023	Contrastive LearningGraph Classification	CodeCode Available	0
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs	Nov 3, 2023	Graph Representation LearningNode Classification	CodeCode Available	0
Cooperative Network Learning for Large-Scale and Decentralized Graphs	Nov 3, 2023	Graph LearningLink Prediction	CodeCode Available	0
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification	Nov 2, 2023	Contrastive LearningNode Classification	—Unverified	0
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach	Oct 29, 2023	Inductive BiasLink Prediction	—Unverified	0
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition	Oct 28, 2023	Node Classification	CodeCode Available	1
BLIS-Net: Classifying and Analyzing Signals on Graphs	Oct 26, 2023	ClassificationGraph Classification	—Unverified	0
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks	Oct 26, 2023	Contrastive LearningGraph Classification	CodeCode Available	0
Graph Neural Networks with a Distribution of Parametrized Graphs	Oct 25, 2023	Graph RegressionNode Classification	—Unverified	0
Resurrecting Label Propagation for Graphs with Heterophily and Label Noise	Oct 25, 2023	DenoisingNode Classification	CodeCode Available	0
Graph Agent: Explicit Reasoning Agent for Graphs	Oct 25, 2023	Graph EmbeddingKnowledge Graphs	—Unverified	0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning	Oct 24, 2023	Contrastive LearningGraph Classification	—Unverified	0
Deceptive Fairness Attacks on Graphs via Meta Learning	Oct 24, 2023	Adversarial RobustnessFairness	CodeCode Available	0
Hierarchical Randomized Smoothing	Oct 24, 2023	Node Classification	—Unverified	0
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks	Oct 23, 2023	Node Classification	—Unverified	0
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels	Oct 23, 2023	Node Classification	—Unverified	0
A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things	Oct 23, 2023	Graph EmbeddingKnowledge Graph Embedding	CodeCode Available	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	Cleora	Accuracy	86.8	—	Unverified
7	NodeNet	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	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	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