在异质多层网络中基于动机的社区检测
Yafang Liu1, Aiwen Li1, An Zeng1
1School of Systems Science, Beijing Normal University, Beijing, 100875, People's Republic of China.
Scientific reports
|April 16, 2024
概括
本研究介绍了一种基于动机的新型算法,用于在异质多层网络中检测社区. 该方法通过最大限度地利用基于图案的模块化来有效地识别社区结构,优于现有的方法.
科学领域:
- 复杂网络分析 复杂网络分析
- 网络科学 网络科学
- 数据挖掘 数据挖掘
背景情况:
- 多层网络在复杂系统中至关重要,具有层内和层间连接.
- 现有的社区检测方法经常忽视多层网络中节点和边缘的异质性.
- 研究主要集中在多重网络上,忽视了具有多种节点和边缘语义的异质多层网络.
研究的目的:
- 解决异质多层网络中社区检测当前方法的局限性.
- 提出一种新的算法,用于识别复杂,异构的多层网络中的社区结构.
- 研究网络主题与这些网络中的社区结构之间的关系.
主要方法:
- 在多层网络中定义社区和图案,包括层间图案.
- 为异质多层网络量身定制的基于图案的模块化措施的开发.
- 通过最大化拟议的基于图案的模块化来实现社区结构检测.
主要成果:
- 基于图案的模块化社区检测算法在合成网络上表现出比经典方法更好的性能.
- 实验结果显示,网络模式和检测到的社区之间存在显著的关系.
- 通过在经验网络上成功实施算法的可用性,验证了算法的可用性,证实了其在现实世界中的实用性.
结论:
- 提出的基于图案的模块化方法为异质多层网络中的社区检测提供了有效的解决方案.
- 这种方法通过结合层间异质性来增强对复杂网络结构的理解.
- 该研究为分析多层网络系统中的异质信息提供了有价值的工具.
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