TY  - JOUR
AV  - public
JF  - Frontiers in Artificial Intelligence
SN  - 2624-8212
TI  - A systematic review of deep learning methods for community detection in social networks
UR  - http://doi.org/10.3389/frai.2025.1572645
ID  - uninipr17844
VL  - 8
KW  - community detection
KW  -  graph clustering
KW  -  deep learning techniques
KW  -  systematic literature
review (SLR)
KW  -  PICO framework
Y1  - 2025/08//
A1  - El-Moussaoui, Mohamed
A1  - Hanine, Mohamed
A1  - Kartit, Ali
A1  - Gracia Villar, Mónica
A1  - Garay, Helena
A1  - de la Torre Díez, Isabel
N2  - 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.
ER  -