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 1201–1225 of 1860 papers

Title	Date	Tasks	Status	Hype
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation	Jun 21, 2021	GPRGraph Neural Network	CodeCode Available	1
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction	Jun 21, 2021	Node Classification	—Unverified	0
Large-Scale Network Embedding in Apache Spark	Jun 20, 2021	Anomaly DetectionLink Prediction	—Unverified	0
RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection	Jun 18, 2021	Fraud DetectionGraph Neural Network	—Unverified	0
Self-supervised Incremental Deep Graph Learning for Ethereum Phishing Scam Detection	Jun 18, 2021	Graph LearningGraph Neural Network	—Unverified	0
MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning	Jun 17, 2021	Graph Representation LearningNode Classification	CodeCode Available	1
SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods	Jun 16, 2021	Node Classification	—Unverified	0
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	Jun 16, 2021	Node ClassificationNode Property Prediction	CodeCode Available	1
On the approximation capability of GNNs in node classification/regression tasks	Jun 16, 2021	ClusteringGraph Classification	CodeCode Available	0
Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification	Jun 16, 2021	ClassificationData Augmentation	—Unverified	0
Zero-shot Node Classification with Decomposed Graph Prototype Network	Jun 15, 2021	ClassificationNode Classification	CodeCode Available	1
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions	Jun 14, 2021	Contrastive LearningGraph Learning	CodeCode Available	0
Node Classification Meets Link Prediction on Knowledge Graphs	Jun 14, 2021	BenchmarkingClassification	—Unverified	0
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification	Jun 12, 2021	ClassificationDrug Design	—Unverified	0
Learnable Hypergraph Laplacian for Hypergraph Learning	Jun 12, 2021	Graph ClassificationNode Classification	CodeCode Available	0
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns	Jun 11, 2021	Graph LearningNode Classification	CodeCode Available	1
Is Homophily a Necessity for Graph Neural Networks?	Jun 11, 2021	Node Classification	—Unverified	0
Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs	Jun 11, 2021	Node Classification	CodeCode Available	2
Learning Based Proximity Matrix Factorization for Node Embedding	Jun 10, 2021	Link PredictionNode Classification	CodeCode Available	0
Learnable Hypergraph Laplacian for Hypergraph Learning	Jun 10, 2021	Graph ClassificationNode Classification	CodeCode Available	0
Automated Self-Supervised Learning for Graphs	Jun 10, 2021	ClusteringNode Classification	CodeCode Available	1
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange	Jun 10, 2021	ClassificationGraph Classification	—Unverified	0
Vertex-Centric Visual Programming for Graph Neural Networks	Jun 9, 2021	Graph ClusteringLink Prediction	—Unverified	0
Fairness-Aware Node Representation Learning	Jun 9, 2021	Contrastive LearningFairness	—Unverified	0
Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach	Jun 9, 2021	Graph ReconstructionNode Classification	CodeCode Available	2

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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	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