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 476–500 of 1860 papers

Title	Date	Tasks	Status	Hype
Hypergraph Transformer for Semi-Supervised Classification	Dec 18, 2023	ClassificationNode Classification	CodeCode Available	1
Dynamic Spiking Framework for Graph Neural Networks	Dec 15, 2023	Dynamic Node ClassificationGraph Representation Learning	—Unverified	0
Hypergraph-MLP: Learning on Hypergraphs without Message Passing	Dec 15, 2023	Node ClassificationRepresentation Learning	CodeCode Available	1
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy	Dec 15, 2023	Deep Reinforcement LearningGraph Neural Network	—Unverified	0
Graph Neural Networks with Diverse Spectral Filtering	Dec 14, 2023	GPRNode Classification	CodeCode Available	1
CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph	Dec 14, 2023	Graph AttentionNode Classification	CodeCode Available	0
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance	Dec 13, 2023	DenoisingNode Classification	CodeCode Available	1
Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-smoothness in Deep GNNs	Dec 13, 2023	Node Classification	CodeCode Available	0
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning	Dec 12, 2023	Contrastive LearningGraph Representation Learning	—Unverified	0
ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network	Dec 10, 2023	Graph Neural NetworkNode Classification	—Unverified	0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning	Dec 9, 2023	Graph ClassificationGraph Learning	—Unverified	0
HC-Ref: Hierarchical Constrained Refinement for Robust Adversarial Training of GNNs	Dec 8, 2023	Node Classification	—Unverified	0
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification	Dec 7, 2023	Graph LearningNode Classification	—Unverified	0
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks	Dec 7, 2023	Graph LearningNode Classification	—Unverified	0
On the Initialization of Graph Neural Networks	Dec 5, 2023	Graph ClassificationGraph Representation Learning	CodeCode Available	0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More	Dec 5, 2023	Adversarial RobustnessMolecular Property Prediction	—Unverified	0
The Self-Loop Paradox: Investigating the Impact of Self-Loops on Graph Neural Networks	Dec 4, 2023	Node Classification	CodeCode Available	0
Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network	Dec 3, 2023	Node Classification	CodeCode Available	0
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks	Nov 30, 2023	Contrastive LearningNode Classification	—Unverified	0
Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs	Nov 29, 2023	Graph Neural NetworkKnowledge Distillation	—Unverified	0
On the Adversarial Robustness of Graph Contrastive Learning Methods	Nov 29, 2023	Adversarial RobustnessContrastive Learning	—Unverified	0
Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training	Nov 27, 2023	Node Classification	—Unverified	0
BHGNN-RT: Network embedding for directed heterogeneous graphs	Nov 24, 2023	Graph Neural NetworkNetwork Embedding	CodeCode Available	0
Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs	Nov 24, 2023	Graph LearningGraph Neural Network	—Unverified	0
Hard Label Black Box Node Injection Attack on Graph Neural Networks	Nov 22, 2023	ClassificationGraph Classification	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	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