对深度学习方法进行系统审查,以在社交网络中检测社区
Mohamed El-Moussaoui1, Mohamed Hanine1, Ali Kartit1
1LTI Laboratory, Department of Telecommunications, Networks, and Informatics, ENSA, Chouaib Doukkali University, El Jadida, Morocco.
Frontiers in artificial intelligence
|September 8, 2025
概括
像图形神经网络这样的深度学习方法对于社交网络中的社区检测是有效的. 这些复杂的网络分析技术的可扩展性和可解释性仍然存在挑战.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 网络分析 网络分析
背景情况:
- 社交网络产生了大量的数据,需要先进的分析方法.
- 深度学习为处理大型数据集和发现复杂模式提供了强大的功能.
研究的目的:
- 在过去的十年中,系统地审查深度学习技术,用于在社交网络中检测社区.
- 确定共同的方法,评估它们的有效性,并突出现有挑战.
主要方法:
- 对19项研究的系统文献综述.
- 从主要的学术数据库 (ACM,Springer,Scopus,Science Direct,IEEE Xplore) 中选择的研究.
- 分析的重点是使用的深度学习模型,社交网络类型,数据集和评估指标.
主要成果:
- 图形神经网络 (GNN),自动编码器和卷积神经网络 (CNN) 是社区检测的突出深度学习方法.
- 在审查的研究中使用了各种社交网络,数据集,评估指标和框架.
结论:
- 可扩展性和可解释性是应用深度学习用于社交网络社区检测的关键挑战.
- 需要进一步的研究来开发适应性解决方案,用于各种网络类型,并提高模型的透明度.
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