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 376–400 of 1860 papers

Title	Date	Tasks	Status	Hype
Deep Learning for Abstract Argumentation Semantics	Jul 15, 2020	Abstract ArgumentationDeep Learning	CodeCode Available	1
MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach	Aug 23, 2020	ClusteringCommunity Detection	CodeCode Available	1
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset	Feb 23, 2022	ArticlesMisinformation	CodeCode Available	1
Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models	Jun 17, 2025	AllNode Classification	CodeCode Available	1
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction	Jun 9, 2022	Graph ClassificationLink Prediction	CodeCode Available	1
Beyond Message Passing: Neural Graph Pattern Machine	Jan 30, 2025	Graph ClassificationGraph Learning	CodeCode Available	1
Neural Message Passing for Quantum Chemistry	Apr 4, 2017	Drug DiscoveryFormation Energy	CodeCode Available	1
Deformable Graph Convolutional Networks	Dec 29, 2021	Node ClassificationNode Classification on Non-Homophilic (Heterophilic) Graphs	CodeCode Available	1
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability	Jun 13, 2024	Graph ClassificationGraph Regression	CodeCode Available	1
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited	Sep 24, 2023	Node Classification	CodeCode Available	1
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks	Jan 28, 2022	Federated LearningNode Classification	CodeCode Available	1
Evaluating Node Embeddings of Complex Networks	Feb 16, 2021	Community DetectionGraph Embedding	CodeCode Available	1
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs	Jun 23, 2021	Knowledge GraphsLink Prediction	CodeCode Available	1
Bayesian Attention Modules	Oct 20, 2020	Image CaptioningMachine Translation	CodeCode Available	1
NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique	Feb 17, 2021	Graph Neural NetworkNode Classification	CodeCode Available	1
A Meta-Learning Approach for Training Explainable Graph Neural Networks	Sep 20, 2021	Graph ClassificationMeta-Learning	CodeCode Available	1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs	Feb 26, 2019	Dynamic Link PredictionEdge Classification	CodeCode Available	1
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs	Jun 8, 2021	Graph Neural NetworkNode Classification	CodeCode Available	1
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs	Mar 17, 2021	BIG-bench Machine LearningGraph Learning	CodeCode Available	1
Disease State Prediction From Single-Cell Data Using Graph Attention Networks	Feb 14, 2020	Disease PredictionGraph Attention	CodeCode Available	1
On the Connection Between MPNN and Graph Transformer	Jan 27, 2023	Graph ClassificationGraph Learning	CodeCode Available	1
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features	Nov 23, 2021	GPUNode Classification	CodeCode Available	1
Examining the Effects of Degree Distribution and Homophily in Graph Learning Models	Jul 17, 2023	BenchmarkingGraph Clustering	CodeCode Available	1
A Deep Graph Wavelet Convolutional Neural Network for Semi-supervised Node Classification	Feb 19, 2021	General ClassificationNode Classification	CodeCode Available	1
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?	Sep 28, 2023	Graph LearningNode Classification	CodeCode Available	1

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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	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