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 226–250 of 1860 papers

Title	Date	Tasks	Status	Hype
Joint Graph Rewiring and Feature Denoising via Spectral Resonance	Aug 13, 2024	DenoisingGraph Learning	CodeCode Available	1
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs	Aug 13, 2024	Dynamic Link PredictionDynamic Node Classification	—Unverified	0
RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling	Aug 13, 2024	Anomaly DetectionClassification	—Unverified	0
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation	Aug 10, 2024	Graph LearningGraph Representation Learning	—Unverified	0
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning	Aug 9, 2024	Graph LearningGraph Representation Learning	—Unverified	0
Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning	Aug 9, 2024	Contrastive LearningNode Classification	CodeCode Available	0
wav2graph: A Framework for Supervised Learning Knowledge Graph from Speech	Aug 8, 2024	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	2
Deep Generative Models for Subgraph Prediction	Aug 7, 2024	Bayesian OptimizationGraph Learning	CodeCode Available	0
Knowledge Probing for Graph Representation Learning	Aug 7, 2024	Graph ClassificationGraph Learning	—Unverified	0
Top K Enhanced Reinforcement Learning Attacks on Heterogeneous Graph Node Classification	Aug 4, 2024	Node Classification	—Unverified	0
Derivation of Back-propagation for Graph Convolutional Networks using Matrix Calculus and its Application to Explainable Artificial Intelligence	Aug 2, 2024	Explainable artificial intelligenceLink Prediction	CodeCode Available	0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck	Aug 1, 2024	Contrastive LearningGraph Representation Learning	—Unverified	0
A Scalable Tool For Analyzing Genomic Variants Of Humans Using Knowledge Graphs and Machine Learning	Jul 30, 2024	Knowledge GraphsNode Classification	CodeCode Available	1
rLLM: Relational Table Learning with LLMs	Jul 29, 2024	ClassificationNode Classification	CodeCode Available	3
UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks	Jul 28, 2024	Data AugmentationNode Classification	CodeCode Available	0
Sharp Bounds for Poly-GNNs and the Effect of Graph Noise	Jul 28, 2024	Node ClassificationStochastic Block Model	—Unverified	0
Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks	Jul 27, 2024	MemorizationNode Classification	—Unverified	0
AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning	Jul 26, 2024	Graph ClassificationKnowledge Graphs	CodeCode Available	1
NC-NCD: Novel Class Discovery for Node Classification	Jul 25, 2024	ClassificationKnowledge Distillation	CodeCode Available	0
Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs	Jul 22, 2024	Few-Shot LearningGraph Neural Network	—Unverified	0
Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing	Jul 21, 2024	ClassificationNode Classification	—Unverified	0
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training	Jul 20, 2024	Contrastive LearningGraph Learning	CodeCode Available	0
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models	Jul 19, 2024	Active LearningKnowledge Graphs	—Unverified	0
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks	Jul 16, 2024	Graph ClassificationNode Classification	—Unverified	0
Relaxing Graph Transformers for Adversarial Attacks	Jul 16, 2024	Adversarial RobustnessGraph Classification	—Unverified	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	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