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Enhancing GNN learning with node augmentation.

Maria Marrium¹, Arif Mahmood¹, Muhammad Haris Khan²

¹Department of Computer Science, Information Technology University, Lahore, Punjab, Pakistan.

Neural Networks : the Official Journal of the International Neural Network Society

|June 16, 2026

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Node-based Augmentation (NAug) synthesizes new graph data samples to improve Graph Neural Networks (GNNs). This novel framework enhances model performance and robustness on diverse graph learning tasks.

Area of Science:

Machine Learning
Graph Neural Networks
Data Augmentation

Background:

Graph Neural Networks (GNNs) excel in various applications but struggle with overfitting and poor generalization on limited or low-diversity graph datasets.
Existing graph data augmentation techniques often use static transformations and do not generate new training samples, limiting their effectiveness.

Purpose of the Study:

To introduce Node-based Augmentation (NAug), a novel framework for synthesizing new data samples to augment training sets for GNNs.
To address the limitations of existing augmentation methods by employing a learning-based generation process that produces semantically and structurally consistent data.

Main Methods:

NAug utilizes two core components: an augmented node feature generator and a link placement predictor.

Keywords:

Data augmentation Graph classification,Graph neural networks Link prediction Node augmentation Node classification

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Published on: December 6, 2024

These components are jointly trained to create new node features and edges, ensuring consistency with the local graph context.

The framework is designed to be versatile, supporting multiple downstream graph learning tasks.

Main Results:

NAug was evaluated on eleven benchmark datasets across node classification, link prediction, and graph classification tasks.
The framework consistently outperformed state-of-the-art augmentation methods.
NAug also demonstrated improvements in machine learning safety measures, including calibration, robustness to noise, and adversarial attacks.

Conclusions:

Node-based Augmentation (NAug) is an effective and versatile framework for general-purpose data augmentation in GNNs.
The learning-based synthesis approach significantly enhances GNN performance and robustness.
NAug offers a promising solution for improving GNNs trained on limited or challenging graph datasets.