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 1226–1250 of 1860 papers

Title	Date	Tasks	Status
Label Efficient Regularization and Propagation for Graph Node Classification	Apr 19, 2022	AttributeBenchmarking	—Unverified
BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction	Apr 18, 2022	AttributeGraph Classification	CodeCode Available
ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification	Apr 15, 2022	Event Causality IdentificationNode Classification	—Unverified
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations	Apr 12, 2022	Community DetectionGraph Representation Learning	CodeCode Available
Multi-view graph structure learning using subspace merging on Grassmann manifold	Apr 11, 2022	Graph ClassificationGraph structure learning	—Unverified
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs	Apr 11, 2022	Node Classification	CodeCode Available
Explicit Feature Interaction-aware Graph Neural Networks	Apr 7, 2022	Graph Neural NetworkNode Classification	CodeCode Available
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020	Apr 6, 2022	AutoMLGPU	CodeCode Available
Synthetic Graph Generation to Benchmark Graph Learning	Apr 4, 2022	Graph GenerationGraph Learning	—Unverified
A Survey on Graph Representation Learning Methods	Apr 4, 2022	Anomaly DetectionGraph Embedding	—Unverified
Message Passing Neural Networks for Hypergraphs	Mar 31, 2022	Graph Neural NetworkHypergraph representations	—Unverified
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation	Mar 30, 2022	ClassificationNode Classification	—Unverified
Supervised Graph Contrastive Learning for Few-shot Node Classification	Mar 29, 2022	ClassificationContrastive Learning	—Unverified
A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks	Mar 23, 2022	ClassificationHierarchical Multi-label Classification	—Unverified
Supervised Contrastive Learning with Structure Inference for Graph Classification	Mar 15, 2022	ClassificationContrastive Learning	—Unverified
Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision	Mar 7, 2022	Node ClassificationSelf-Supervised Learning	CodeCode Available
Shift-Robust Node Classification via Graph Adversarial Clustering	Mar 7, 2022	ClassificationClustering	—Unverified
Deep Partial Multiplex Network Embedding	Mar 5, 2022	Link PredictionNetwork Embedding	—Unverified
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks	Mar 3, 2022	Community DetectionLink Prediction	—Unverified
Graph Representation Learning Beyond Node and Homophily	Mar 3, 2022	Edge ClassificationGraph Embedding	CodeCode Available
Exploring Edge Disentanglement for Node Classification	Feb 23, 2022	ClassificationDisentanglement	—Unverified
When Does A Spectral Graph Neural Network Fail in Node Classification?	Feb 16, 2022	Graph LearningGraph Neural Network	—Unverified
Adversarial Graph Contrastive Learning with Information Regularization	Feb 14, 2022	Contrastive LearningData Augmentation	CodeCode Available
Geometric Graph Representation Learning via Maximizing Rate Reduction	Feb 13, 2022	Community DetectionContrastive Learning	—Unverified
Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network	Feb 13, 2022	Graph Neural NetworkLink Prediction	—Unverified

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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