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 551–575 of 1860 papers

Title	Date	Tasks	Status	Score
Distilling Influences to Mitigate Prediction Churn in Graph Neural Networks	Oct 2, 2023	Knowledge DistillationNode Classification	CodeCode Available	5
Improvements on Uncertainty Quantification for Node Classification via Distance-Based Regularization	Nov 10, 2023	Node ClassificationOut of Distribution (OOD) Detection	CodeCode Available	5
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms	Oct 3, 2024	Graph Representation LearningNode Classification	CodeCode Available	5
Dissimilar Nodes Improve Graph Active Learning	Dec 5, 2022	Active LearningGraph Embedding	CodeCode Available	5
Disparity, Inequality, and Accuracy Tradeoffs in Graph Neural Networks for Node Classification	Aug 18, 2023	FairnessNode Classification	CodeCode Available	5
BiasedWalk: Biased Sampling for Representation Learning on Graphs	Sep 7, 2018	Community DetectionGeneral Classification	CodeCode Available	5
Hypergraph Neural Networks for Hypergraph Matching	Jan 1, 2021	Graph MatchingHypergraph Matching	CodeCode Available	5
BHGNN-RT: Network embedding for directed heterogeneous graphs	Nov 24, 2023	Graph Neural NetworkNetwork Embedding	CodeCode Available	5
Hyperedge Anomaly Detection with Hypergraph Neural Network	Dec 7, 2024	Anomaly DetectionLink Prediction	CodeCode Available	5
Disambiguated Node Classification with Graph Neural Networks	Feb 13, 2024	ClassificationContrastive Learning	CodeCode Available	5
Article Classification with Graph Neural Networks and Multigraphs	Sep 20, 2023	ArticlesClassification	CodeCode Available	5
Advancing GraphSAGE with A Data-Driven Node Sampling	Apr 29, 2019	General ClassificationGraph Neural Network	CodeCode Available	5
kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning	Dec 4, 2022	Link PredictionNode Classification	CodeCode Available	5
An Experimental Study of the Transferability of Spectral Graph Networks	Dec 18, 2020	BenchmarkingGeneral Classification	CodeCode Available	5
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification	Apr 11, 2023	Graph Representation LearningNode Classification	CodeCode Available	5
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs	Jun 18, 2022	AttributeNode Classification	CodeCode Available	5
Beyond Observed Connections : Link Injection	Sep 2, 2020	Link PredictionNode Classification	CodeCode Available	5
HyperAggregation: Aggregating over Graph Edges with Hypernetworks	Jul 16, 2024	Graph ClassificationGraph Regression	CodeCode Available	5
Dimensional Reweighting Graph Convolutional Networks	Jul 4, 2019	Node Classification	CodeCode Available	5
Finding Counterfactual Evidences for Node Classification	May 16, 2025	Classificationcounterfactual	CodeCode Available	5
High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approach	Nov 4, 2024	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	5
Dimensionality Reduction Meets Message Passing for Graph Node Embeddings	Feb 1, 2022	Dimensionality ReductionNode Classification	CodeCode Available	5
HoloNets: Spectral Convolutions do extend to Directed Graphs	Oct 3, 2023	Graph LearningNode Classification	CodeCode Available	5
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds	Jun 15, 2023	Graph ReconstructionGraph Representation Learning	CodeCode Available	5
Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters	Jun 29, 2023	Node Classification	CodeCode Available	5

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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