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 151–175 of 1860 papers

Title	Date	Tasks	Status	Hype	Score
DRew: Dynamically Rewired Message Passing with Delay	May 13, 2023	Graph ClassificationGraph Regression	CodeCode Available	1	5
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling	Mar 21, 2022	Node Classification	CodeCode Available	1	5
A Generalization of Transformer Networks to Graphs	Dec 17, 2020	Graph RegressionInductive Bias	CodeCode Available	1	5
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning	Mar 22, 2023	Dynamic Link PredictionDynamic Node Classification	CodeCode Available	1	5
Bilinear Graph Neural Network with Neighbor Interactions	Feb 10, 2020	General ClassificationGraph Neural Network	CodeCode Available	1	5
Binary Graph Convolutional Network with Capacity Exploration	Oct 24, 2022	BinarizationNode Classification	CodeCode Available	1	5
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	5
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters	Aug 19, 2020	Anomaly DetectionFraud Detection	CodeCode Available	1	5
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance	Dec 13, 2023	DenoisingNode Classification	CodeCode Available	1	5
Boosting Multitask Learning on Graphs through Higher-Order Task Affinities	Jun 24, 2023	Community DetectionNode Classification	CodeCode Available	1	5
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	5
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs	Feb 26, 2019	Dynamic Link PredictionEdge Classification	CodeCode Available	1	5
A Survey of Adversarial Learning on Graphs	Mar 10, 2020	ClusteringGraph Clustering	CodeCode Available	1	5
Explaining the Explainers in Graph Neural Networks: a Comparative Study	Oct 27, 2022	Node Classification	CodeCode Available	1	5
Diffusion Mechanism in Residual Neural Network: Theory and Applications	May 7, 2021	Binary ClassificationClassification	CodeCode Available	1	5
Exploring Graph Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation	Feb 26, 2025	BenchmarkingGraph Learning	CodeCode Available	1	5
Deformable Graph Convolutional Networks	Dec 29, 2021	Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs	CodeCode Available	1	5
Bayesian Attention Modules	Oct 20, 2020	Image CaptioningMachine Translation	CodeCode Available	1	5
Augmentation-Free Self-Supervised Learning on Graphs	Dec 5, 2021	Node ClassificationSelf-Supervised Learning	CodeCode Available	1	5
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling	Jan 30, 2018	Node Classification	CodeCode Available	1	5
A Unified Lottery Ticket Hypothesis for Graph Neural Networks	Feb 12, 2021	Link PredictionNode Classification	CodeCode Available	1	5
Adaptive Graph Diffusion Networks	Dec 30, 2020	GPULink Prediction	CodeCode Available	1	5
Can GNN be Good Adapter for LLMs?	Feb 20, 2024	Graph Neural NetworkNode Classification	CodeCode Available	1	5
Fast Sequence Based Embedding with Diffusion Graphs	Mar 20, 2018	ClusteringCommunity Detection	CodeCode Available	1	5
Diffusion Improves Graph Learning	Oct 28, 2019	ClusteringGraph Learning	CodeCode Available	1	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	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	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	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