使用隐藏的马尔科夫模型检测网络图案
Costas Bampos1, Vasileios Megalooikonomou2
1Computer Engineering and Informatics Department, School of Engineering, University of Patras, Patras, Greece. costas.bampos@gmail.com.
Scientific reports
|November 25, 2025
概括
本研究介绍了用于网络动机检测的隐藏马尔科夫模型 (HMM),使复杂网络中经常出现的子图模式能够准确识别,即使有噪音数据. 新的HMM方法为分析网络结构提供了一个概率框架.
科学领域:
- 计算生物学 计算生物学
- 网络科学 网络科学
- 机器学习 机器学习
背景情况:
- 复杂的网络是使用顶点和边缘建模的,反复出现的子图 (图案) 揭示了组织原理.
- 现有的网络模式检测方法往往缺乏对丢失或杂数据的稳定性.
研究的目的:
- 引入隐藏马尔科夫模型 (HMM) 的新型应用,用于网络图案检测.
- 开发一个概率评分框架来识别网络动机,耐受缺失或噪音边缘.
主要方法:
- 将子图编码为简短的符号序列.
- 使用标准的HMM内核 (Viterbi/Forward) 来进行分数序列.
- 将HMM管道应用于253节点定向基准网络.
主要成果:
- 该HMM管道实现了与精确计数可比的精确度,用于恢复已知的4节点模式.
- 该方法提供分级的概率,容忍网络数据中的缺失或噪音边缘.
- 与现有的工具 (ESU,FANMOD,G-Tries) 进行了复杂性比较.
结论:
- 这项工作是首次将HMM应用于网络图案检测的应用.
- 开发的HMM方法为网络分析提供了一个实用,概率和重量意识的框架.
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