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 76–100 of 1860 papers

Title	Date	Tasks	Status	Hype
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs	Mar 31, 2024	Hypergraph representationsNode Classification	CodeCode Available	1
Open-World Semi-Supervised Learning for Node Classification	Mar 18, 2024	ClassificationContrastive Learning	CodeCode Available	1
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts	Mar 8, 2024	Link PredictionNode Classification	CodeCode Available	1
Pairwise Alignment Improves Graph Domain Adaptation	Mar 2, 2024	Domain AdaptationGRAPH DOMAIN ADAPTATION	CodeCode Available	1
Polynormer: Polynomial-Expressive Graph Transformer in Linear Time	Mar 2, 2024	Node Classification	CodeCode Available	1
Graph Parsing Networks	Feb 22, 2024	Graph ClassificationGraph Reconstruction	CodeCode Available	1
Can GNN be Good Adapter for LLMs?	Feb 20, 2024	Graph Neural NetworkNode Classification	CodeCode Available	1
SimMLP: Training MLPs on Graphs without Supervision	Feb 14, 2024	Fraud DetectionGraph Classification	CodeCode Available	1
HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs	Feb 11, 2024	Inductive BiasLanguage Modeling	CodeCode Available	1
Masked Graph Autoencoder with Non-discrete Bandwidths	Feb 6, 2024	BlockingLink Prediction	CodeCode Available	1
Topology-Informed Graph Transformer	Feb 3, 2024	Graph ClassificationGraph Regression	CodeCode Available	1
Graph Contrastive Invariant Learning from the Causal Perspective	Jan 23, 2024	Contrastive LearningNode Classification	CodeCode Available	1
Disentangled Condensation for Large-scale Graphs	Jan 18, 2024	Graph LearningLink Prediction	CodeCode Available	1
Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures	Dec 28, 2023	Node ClassificationNode Clustering	CodeCode Available	1
PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering	Dec 22, 2023	Graph Representation LearningNode Classification	CodeCode Available	1
Hypergraph Transformer for Semi-Supervised Classification	Dec 18, 2023	ClassificationNode Classification	CodeCode Available	1
Graph Transformers for Large Graphs	Dec 18, 2023	Graph LearningGraph Property Prediction	CodeCode Available	1
Hypergraph-MLP: Learning on Hypergraphs without Message Passing	Dec 15, 2023	Node ClassificationRepresentation Learning	CodeCode Available	1
Graph Neural Networks with Diverse Spectral Filtering	Dec 14, 2023	GPRNode Classification	CodeCode Available	1
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance	Dec 13, 2023	DenoisingNode Classification	CodeCode Available	1
Mixture of Weak & Strong Experts on Graphs	Nov 9, 2023	Graph Neural NetworkNode Classification	CodeCode Available	1
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition	Oct 28, 2023	Node Classification	CodeCode Available	1
SplitGNN: Spectral Graph Neural Network for Fraud Detection against Heterophily	Oct 21, 2023	Fraud DetectionGraph Neural Network	CodeCode Available	1
Pretraining Language Models with Text-Attributed Heterogeneous Graphs	Oct 19, 2023	Graph Neural NetworkLink Prediction	CodeCode Available	1
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphs	Oct 15, 2023	Few-Shot LearningGraph Learning	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	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