El-Moussaoui Mohamed, Hanine Mohamed, Kartit Ali, Villar Monica Garcia, Garay Helena, de la Torre Díez Isabel
LTI Laboratory, Department of Telecommunications, Networks, and Informatics, ENSA, Chouaib Doukkali University, El Jadida, Morocco.
Universidad Europea del Atlantico, Santander, Spain.
Front Artif Intell. 2025 Aug 22;8:1572645. doi: 10.3389/frai.2025.1572645. eCollection 2025.
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.
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.
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.
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.
通过社交网络生成的数据迅速膨胀带来了重大挑战，这凸显了需要先进方法来分析和解释这些复杂系统的必要性。深度学习已成为一种有效的方法，具有处理大型数据集以及揭示复杂关系和模式的强大能力。
在本系统文献综述中，我们探讨了过去十年进行的研究，重点关注深度学习技术在社交网络社区检测中的应用。从包括ACM数字图书馆、Springer Link、Scopus、Science Direct和IEEE Xplore在内的知名数据库中精心挑选了19项研究。本综述调查了所采用的方法，评估了它们的有效性，并讨论了这些研究中发现的挑战。
我们的综述表明，图神经网络（GNN）、自动编码器和卷积神经网络（CNN）等模型是社区检测中一些最常用的方法。它还研究了这些研究中社交网络、数据集、评估指标和所采用框架的多样性。
然而，分析突出了几个挑战，如可扩展性、理解模型如何工作（可解释性）以及需要能够适应不同类型网络的解决方案。这些问题是需要进一步关注和深入研究的重要领域。本综述为从事社交网络分析的研究人员提供了有意义的见解。它提供了近期发展的详细总结，展示了最具影响力的深度学习方法，并确定了有待探索的关键挑战。
