使用有序随机图模型找到社区结构
Masaki Ochi1, Tatsuro Kawamoto2
1Department of Physics, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8574, Japan.
Physical review. E
|August 16, 2023
概括
这项研究引入了一种新的算法来排序网络矩阵. 该方法改善了社区结构的可视化,比现有技术更清楚地揭示了网络组件.
科学领域:
- 网络科学 网络科学
- 图形理论是指图形的理论.
- 数据可视化数据可视化
背景情况:
- 邻近矩阵可视化揭示了网络宏观特征.
- 社区结构,以密集的组件为特征,以块对角形形式出现.
- 经典的排序算法难以对准可见社区结构的矩阵.
研究的目的:
- 为邻近矩阵开发一种新的排序算法.
- 提高社区结构在网络中的可见性.
- 在识别网络社区方面超越现有的排序算法.
主要方法:
- 提出了一个基于最大概率估计的排序算法.
- 使用有序随机图模型对矩阵元素对齐.
- 将拟议的方法与经典的排序算法进行比较.
主要成果:
- 拟议的算法有效地对准矩阵元素.
- 社区结构变得更加清晰地识别.
- 与现有方法相比,在社区结构检测方面表现优越.
结论:
- 新的订单算法增强了网络可视化.
- 最大概率估计为矩阵排序提供了一个有效的方法.
- 这种方法可以更好地识别复杂网络中的社区结构.
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