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

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Showing 1651–1675 of 1860 papers

Title	Date	Tasks	Status
Positive-Unlabeled Node Classification with Structure-aware Graph Learning	Oct 20, 2023	Anomaly DetectionClassification	—Unverified
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields	Nov 8, 2024	Graph Representation LearningNode Classification	—Unverified
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning	Jan 14, 2025	Abstention PredictionDynamic Link Prediction	—Unverified
Predicting the structure of dynamic graphs	Jan 8, 2024	Incremental LearningLink Prediction	—Unverified
Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs	Jul 22, 2024	Few-Shot LearningGraph Neural Network	—Unverified
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification	May 25, 2020	ClassificationGeneral Classification	—Unverified
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective	Jul 3, 2021	Link PredictionNode Classification	—Unverified
Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs	Nov 29, 2023	Graph Neural NetworkKnowledge Distillation	—Unverified
Propagation with Adaptive Mask then Training for Node Classification on Attributed Networks	Jun 21, 2022	AttributeNode Classification	—Unverified
Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks	Sep 22, 2023	Node Classification	—Unverified
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More	Dec 5, 2023	Adversarial RobustnessMolecular Property Prediction	—Unverified
Quantifying Challenges in the Application of Graph Representation Learning	Jun 18, 2020	Graph Representation LearningLink Prediction	—Unverified
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations	Aug 31, 2021	Autonomous Drivingimage-classification	—Unverified
Quantum-based subgraph convolutional neural networks	Apr 1, 2019	General ClassificationGraph Classification	—Unverified
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs	Dec 12, 2020	Adversarial AttackGraph Classification	—Unverified
QUINT: Node embedding using network hashing	Sep 9, 2021	Link PredictionNetwork Embedding	—Unverified
RECS: Robust Graph Embedding Using Connection Subgraphs	May 3, 2018	General ClassificationGraph Embedding	—Unverified
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models	Mar 13, 2024	ClassificationData Augmentation	—Unverified
Refined Edge Usage of Graph Neural Networks for Edge Prediction	Dec 25, 2022	Link PredictionNode Classification	—Unverified
REFINE: Random RangE FInder for Network Embedding	Aug 24, 2021	Network EmbeddingNode Classification	—Unverified
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks	Dec 27, 2023	Contrastive LearningNode Classification	—Unverified
ReGrAt: Regularization in Graphs using Attention to handle class imbalance	Nov 27, 2022	ClassificationNode Classification	—Unverified
Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding	Sep 23, 2022	Graph LearningGraph Neural Network	—Unverified
Relation Structure-Aware Heterogeneous Information Network Embedding	May 15, 2019	ClusteringLink Prediction	—Unverified
Relaxing Graph Transformers for Adversarial Attacks	Jul 16, 2024	Adversarial RobustnessGraph Classification	—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	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	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