Harnessing the Power of Ensembled Deep Learning and Graph Neural Networks for Multidimensional Insights

Paramjot Kaur Sarao, Manish Sharma, Anupriya

2025

Abstract

Ensemble methods have long been recognized for their ability to enhance the performance and robustness of machine learning models. With the advent of deep learning and Graph Neural Networks (GNNs), the integration of ensemble techniques has opened new avenues for research and application. This paper explores the synergistic potential of combining deep learning ensembles with graph neural networks (GNNs) to enhance performance on complex graph-structured data tasks. The paper first examines traditional ensembling methods adapted for deep learning, including bagging, boosting, and stacking approaches tailored to neural architectures. We then delve into novel ensemble techniques specifically designed for GNNs, addressing the unique challenges posed by graph-structured data. This covers diverse applications, from computer vision and natural language processing to recommendation systems and bio-informatics. It concludes by identifying open challenges, promising research directions, and potential real-world impacts of ensembling deep learning and GNN models, providing a roadmap for future work in this rapidly evolving field.

Paper Citation

in Harvard Style

Sarao P., Sharma M. and Anupriya. (2025). Harnessing the Power of Ensembled Deep Learning and Graph Neural Networks for Multidimensional Insights. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 203-211. DOI: 10.5220/0013589400004664

in Bibtex Style

@conference{incoft25,
author={Paramjot Kaur Sarao and Manish Sharma and Anupriya},
title={Harnessing the Power of Ensembled Deep Learning and Graph Neural Networks for Multidimensional Insights},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={203-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013589400004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Harnessing the Power of Ensembled Deep Learning and Graph Neural Networks for Multidimensional Insights
SN - 978-989-758-763-4
AU - Sarao P.
AU - Sharma M.
AU - Anupriya.
PY - 2025
SP - 203
EP - 211
DO - 10.5220/0013589400004664
PB - SciTePress