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 526–550 of 1860 papers

Title	Date	Tasks	Status	Score
On the approximation capability of GNNs in node classification/regression tasks	Jun 16, 2021	ClusteringGraph Classification	CodeCode Available	5
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification	Apr 11, 2023	Graph Representation LearningNode Classification	CodeCode Available	5
Hyperedge Anomaly Detection with Hypergraph Neural Network	Dec 7, 2024	Anomaly DetectionLink Prediction	CodeCode Available	5
Transferring Robustness for Graph Neural Network Against Poisoning Attacks	Aug 20, 2019	Graph Neural NetworkNode Classification	CodeCode Available	5
Multi-View Empowered Structural Graph Wordification for Language Models	Jun 19, 2024	Graph Neural NetworkNode Classification	CodeCode Available	5
Deperturbation of Online Social Networks via Bayesian Label Transition	Oct 27, 2020	Node Classification	CodeCode Available	5
DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)	Dec 21, 2023	Node Classification	CodeCode Available	5
Domain-adaptive Message Passing Graph Neural Network	Aug 31, 2023	Domain AdaptationGRAPH DOMAIN ADAPTATION	CodeCode Available	5
Domain Adaptive Graph Infomax via Conditional Adversarial Networks	Jan 1, 2023	Node Classification	CodeCode Available	5
A novel robust integrating method by high-order proximity for self-supervised attribute network embedding	Dec 10, 2024	AttributeData Visualization	CodeCode Available	5
HyperAggregation: Aggregating over Graph Edges with Hypernetworks	Jul 16, 2024	Graph ClassificationGraph Regression	CodeCode Available	5
Article Classification with Graph Neural Networks and Multigraphs	Sep 20, 2023	ArticlesClassification	CodeCode Available	5
Binarized Attributed Network Embedding	Oct 22, 2018	Graph EmbeddingLink Prediction	CodeCode Available	5
Distribution Free Prediction Sets for Node Classification	Nov 26, 2022	ClassificationConformal Prediction	CodeCode Available	5
A novel measure to identify influential nodes: Return Random Walk Gravity Centrality	May 1, 2023	Approximating Betweenness-Centrality rankingNode Classification	CodeCode Available	5
Billion-scale Network Embedding with Iterative Random Projection	May 7, 2018	Distributed ComputingLink Prediction	CodeCode Available	5
Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks	Nov 7, 2024	Graph MiningNode Classification	CodeCode Available	5
Hierarchical graph sampling based minibatch learning with chain preservation and variance reduction	Mar 2, 2025	Graph SamplingNode Classification	CodeCode Available	5
Distilling Influences to Mitigate Prediction Churn in Graph Neural Networks	Oct 2, 2023	Knowledge DistillationNode Classification	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
Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding	Dec 28, 2023	Graph EmbeddingLink Prediction	CodeCode Available	5
BHGNN-RT: Network embedding for directed heterogeneous graphs	Nov 24, 2023	Graph Neural NetworkNetwork Embedding	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	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	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	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