Graph Convolutional Networks and Particle Competition and Cooperation for Semi-Supervised Learning

Gustavo Rosseto Leticio, Matheus Henrique Jacob dos Santos, Lucas Valem, Vinicius Atsushi Sato Kawai, Fabricio Aparecido Breve, Daniel Pedronette

2025

Abstract

Given the substantial challenges associated with obtaining labeled data, including high costs, time consumption, and the frequent need for expert involvement, semi-supervised learning has garnered increased attention. In these scenarios, Graph Convolutional Networks (GCNs) offer an attractive and promising solution, as they can effectively leverage labeled and unlabeled data for classification. Through their ability to capture complex relationships within data, GCNs provide a powerful framework for tasks that rely on limited labeled information. There are also other promising approaches that exploit the graph structure for more effective learning, such as the Particle Competition and Cooperation (PCC), an algorithm that models label propagation through particles that compete and cooperate on a graph constructed from the data, exploiting similarity relationships between instances. In this work, we propose a novel approach that combines PCC, GCN, and dimensionality reduction approaches for improved classification performance. The experimental results showed that our method provided gains in most cases.

Paper Citation

in Harvard Style

Leticio G., Santos M., Valem L., Kawai V., Breve F. and Pedronette D. (2025). Graph Convolutional Networks and Particle Competition and Cooperation for Semi-Supervised Learning. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 519-526. DOI: 10.5220/0013267000003912

in Bibtex Style

@conference{visapp25,
author={Gustavo Leticio and Matheus Santos and Lucas Valem and Vinicius Kawai and Fabricio Breve and Daniel Pedronette},
title={Graph Convolutional Networks and Particle Competition and Cooperation for Semi-Supervised Learning},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={519-526},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013267000003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Graph Convolutional Networks and Particle Competition and Cooperation for Semi-Supervised Learning
SN - 978-989-758-728-3
AU - Leticio G.
AU - Santos M.
AU - Valem L.
AU - Kawai V.
AU - Breve F.
AU - Pedronette D.
PY - 2025
SP - 519
EP - 526
DO - 10.5220/0013267000003912
PB - SciTePress