通过从噪音数据中生成一个网络变体池来实现网络分析
Aamir Mandviwalla1,2, Amr Elsisy1,2, Muhammad Saad Atique1,2
1Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
本研究引入了一个生成模型,从噪音数据中推断出隐藏的社交网络中的隐藏结构和功能. 它通过生成统计学上相当的网络来识别持久模式,减少网络运行的不确定性.
科学领域:
- 网络科学 网络科学
- 社交网络分析 社交网络分析
- 信息安全 信息安全
背景情况:
- 了解网络结构和功能至关重要,特别是在有意掩盖其拓学的秘密社交网络中.
- 从不完整或杂的数据中推断网络属性存在重大挑战.
研究的目的:
- 开发一种可靠的方法来利用噪音数据推断秘密社交网络的结构和功能.
- 解决当地面真相不可用时,单个网络表示的局限性.
主要方法:
- 应用一个生成模型,从噪音数据中创建一个统计学上相当的网络池.
- 使用网络变量重复计数来近似现实世界的概率.
- 采用香农来识别网络变体的最小不确定性.
主要成果:
- 在频繁发生的网络变体中确定了持久的结构模式.
- 证明从最好的网络变体重复生成可以降低不确定性 ().
- 提出了一个启发式的构建最优的网络变体与最小化的运营成本.
结论:
- 生成模型有效地推断出秘密网络结构和功能,尽管有噪音数据.
- 香农是量化和减少网络分析中的不确定性的一个有价值的指标.
- 该方法为网络运营商提供了一种实用的方法,以确定监控的关键节点.
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