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 776–800 of 1860 papers

Title	Date	Tasks	Status	Score
Connector 0.5: A unified framework for graph representation learning	Apr 25, 2023	Graph EmbeddingGraph Generation	CodeCode Available	5
A Capsule Network-based Model for Learning Node Embeddings	Nov 12, 2019	General ClassificationNode Classification	CodeCode Available	5
Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information	Sep 29, 2020	General ClassificationNode Classification	CodeCode Available	5
L^2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification	Mar 10, 2024	Node Classification	CodeCode Available	5
IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors	May 2, 2024	Data AugmentationNode Classification	CodeCode Available	5
Integrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex	Apr 16, 2025	Link PredictionNode Classification	CodeCode Available	5
DeltaGNN: Graph Neural Network with Information Flow Control	Jan 10, 2025	Graph Neural NetworkNode Classification	CodeCode Available	5
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning	Dec 22, 2021	Link PredictionLink Property Prediction	CodeCode Available	5
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information Aggregation	Jan 18, 2024	Graph LearningNode Classification	CodeCode Available	5
From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis	Nov 17, 2024	Graph RegressionHeterogeneous Node Classification	CodeCode Available	5
Information Extraction from Visually Rich Documents Using Directed Weighted Graph Neural Network	Sep 11, 2024	Document Layout Analysisdocument understanding	CodeCode Available	5
Investigating the Interplay between Features and Structures in Graph Learning	Aug 18, 2023	Graph LearningInductive Bias	CodeCode Available	5
From Node Embedding To Community Embedding	Oct 31, 2016	Graph EmbeddingNode Classification	CodeCode Available	5
Node Embedding over Temporal Graphs	Mar 21, 2019	ClusteringLink Prediction	CodeCode Available	5
Node Embedding with Adaptive Similarities for Scalable Learning over Graphs	Nov 27, 2018	ClusteringCommunity Detection	CodeCode Available	5
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs	Apr 11, 2022	Node Classification	CodeCode Available	5
It Takes a Graph to Know a Graph: Rewiring for Homophily with a Reference Graph	May 18, 2025	Node Classification	CodeCode Available	5
From ChebNet to ChebGibbsNet	Dec 2, 2024	Graph Representation LearningNode Classification	CodeCode Available	5
Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling	May 19, 2021	ClusteringGPU	CodeCode Available	5
Asymptotics of Network Embeddings Learned via Subsampling	Jul 6, 2021	Link PredictionNode Classification	CodeCode Available	5
Framework for Designing Filters of Spectral Graph Convolutional Neural Networks in the Context of Regularization Theory	Sep 29, 2020	Graph LearningNode Classification	CodeCode Available	5
A Systematic Evaluation of Node Embedding Robustness	Sep 16, 2022	ClassificationNode Classification	CodeCode Available	5
Independent Distribution Regularization for Private Graph Embedding	Aug 16, 2023	AttributeGraph Embedding	CodeCode Available	5
Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph	Aug 22, 2019	Deep LearningKnowledge Graphs	CodeCode Available	5
iN2V: Bringing Transductive Node Embeddings to Inductive Graphs	Jun 5, 2025	Node ClassificationRepresentation Learning	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	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