在使用Voronoi分区的定向加权网络中进行社区检测
Botond Molnár1,2,3, Ildikó-Beáta Márton1, Szabolcs Horvát4,5,6
1Faculty of Mathematics and Computer Science, Babeș-Bolyai University, 400084, Cluj-Napoca, Romania.
Scientific reports
|April 6, 2024
概括
我们开发了一种新的社区检测算法,用于定向和加权网络,其性能优于现有的方法. 这种方法有效地识别网络结构,包括大脑连接中的等级模式.
科学领域:
- 网络分析 网络分析
- 图形理论是指图形的理论.
- 计算拓学的计算拓.
背景情况:
- 社区检测在网络分析中至关重要,但对于有针对性和加权网络来说具有挑战性.
- 现有的算法与高图形密度和多样化的链接信息作斗争.
- 链接重量和图形密度对当前社区检测方法构成重大挑战.
研究的目的:
- 介绍一种新的算法,用于在指导和加权网络中检测社区.
- 为复杂的网络结构推广沃罗诺伊分区.
- 提供一种直接使用边缘权重表示长度的方法.
主要方法:
- 一个通用的Voronoi分区算法,应用于有针对性的加权网络.
- 直接整合边缘重量,包括代表长度的边缘重量.
- 对基准和现实世界网络的既定算法进行比较性性能分析.
主要成果:
- 该算法有效地检测密集图形中的社区,其中权重是主要的.
- 通过大脑连接成功识别了等级网络结构,以大脑连接为例.
- 证明了与最先进的算法相比的或更高的时间效率.
结论:
- 拟议的Voronoi分区算法为定向和加权网络中的社区检测提供了一个有效的解决方案.
- 该方法处理复杂的网络数据,包括基于长度的权重和层次结构.
- 该算法在神经科学,运输和社交网络分析等领域的应用方面表现有前途.
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