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primary topic: A systematic review of deep learning methods for community detection in social networks

A systematic review of deep learning methods for community detection in social networks
type: AcademicArticle
type: Article
abstract: Introduction: The rapid expansion of generated data through social networks has introduced significant challenges, which underscores the need for advanced methods to analyze and interpret these complex systems. Deep learning has emerged as an effective approach, offering robust capabilities to process large datasets, and uncover intricate relationships and patterns. Methods: In this systematic literature review, we explore research conducted over the past decade, focusing on the use of deep learning techniques for community detection in social networks. A total of 19 studies were carefully selected from reputable databases, including the ACM Library, Springer Link, Scopus, Science Direct, and IEEE Xplore. This review investigates the employed methodologies, evaluates their effectiveness, and discusses the challenges identified in these works. Results: Our review shows that models like graph neural networks (GNNs), autoencoders, and convolutional neural networks (CNNs) are some of the most commonly used approaches for community detection. It also examines the variety of social networks, datasets, evaluation metrics, and employed frameworks in these studies. Discussion: However, the analysis highlights several challenges, such as scalability, understanding how the models work (interpretability), and the need for solutions that can adapt to different types of networks. These issues stand out as important areas that need further attention and deeper research. This review provides meaningful insights for researchers working in social network analysis. It offers a detailed summary of recent developments, showcases the most impactful deep learning methods, and identifies key challenges that remain to be explored.
date: 2025-08
volume: 8
is part of: Frontiers in Artificial Intelligence
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: A systematic review of deep learning methods for community detection in social networks (Texto)
has document: A systematic review of deep learning methods for community detection in social networks (Otro)
subject: Engineering

A systematic review of deep learning methods for community detection in social networks (Texto)
type: Document
has file: frai-1-1572645.pdf
has part: frai-1-1572645.pdf

Frontiers in Artificial Intelligence
type: Collection
issn: 26248212

Mohamed Hanine
type: Person
given name: Mohamed
family name: Hanine

Helena Garay
type: Person
given name: Helena
family name: Garay

Mónica Gracia Villar
type: Person
given name: Mónica
family name: Gracia Villar

Isabel de la Torre Díez
type: Person
given name: Isabel
family name: de la Torre Díez

Mohamed El-Moussaoui
type: Person
given name: Mohamed
family name: El-Moussaoui

Ali Kartit
type: Person
given name: Ali
family name: Kartit

A systematic review of deep learning methods for community detection in social networks (Otro)
type: Document
is version of: A systematic review of deep learning methods for community detection in social networks (Texto)
is volatile version of: A systematic review of deep learning methods for community detection in social networks (Texto)
is index codes version of: A systematic review of deep learning methods for community detection in social networks (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #17844 A systematic review of deep learning methods for community detection in social networks
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primary topic: A systematic review of deep learning methods for community detection in social networks

Engineering
type: Concept
preferred label: Ingeniería @es