在线贝叶斯变化点检测网络Poisson进程与社区结构的社区结构
Joshua Corneck1, Edward A K Cohen1, James S Martin1
1Department of Mathematics, Imperial College London, 180 Queen's Gate, London, SW7 2AZ UK.
概括
本研究介绍了一种在线方法,用于检测网络点过程结构的变化. 该方法准确地识别了实时隐性节点会员和边缘进程速率的变化.
科学领域:
- 统计 统计 统计 统计
- 网络分析 网络分析
- 机器学习 机器学习
背景情况:
- 网络点进程通常具有影响其行为的底层结构.
- 这些潜在结构并不总是静态的,因此变化检测至关重要.
- 识别网络动态的变化对于理解复杂系统至关重要.
研究的目的:
- 开发一种新的在线方法,用于检测网络点过程中隐藏结构的变化.
- 为了应对网络分析中动态潜伏结构的挑战.
- 为实时网络变更检测提供可扩展和准确的方法.
主要方法:
- 专注于具有潜伏节点会员的块同质的Poisson过程.
- 为在线分析提出一个可扩展的变量程序.
- 使用贝叶斯式遗忘因子来进行顺序变量近似.
- 将框架应用于模拟和现实世界的网络数据.
主要成果:
- 拟议的框架可以快速准确地检测潜伏边缘过程速率的变化.
- 它有效地在线识别潜伏节点组成员的变化.
- 在模拟数据和现实世界共享自行车网络上证明了有效性.
结论:
- 开发的在线方法对于检测网络点过程结构的动态变化是有效的.
- 该方法准确地捕捉了网络行为的实时变化,包括与外部因素相关的变化.
- 该框架为分析不断变化的网络现象提供了有价值的工具.
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