基于成本意识的来源采样之间的间隔性进行了通用的网络拆解
Jihui Han1, Chengyi Zhang1, Gaogao Dong2
1School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, Henan, China.
Chaos (Woodbury, N.Y.)
|May 2, 2025
概括
我们开发了一种新的算法,即成本意识源采样间隔 (CASS-Bet),用于高效的网络拆解. 这种方法平衡了节点的重要性和移除成本,在基础设施保护和犯罪控制方面表现优越.
科学领域:
- 网络科学 网络科学
- 计算机科学 计算机科学
- 应用数学 应用数学 应用数学
背景情况:
- 网络拆解对于基础设施保护和流行病控制等关键应用至关重要.
- 当前的方法经常在现实场景中与效率和成本意识作斗争.
研究的目的:
- 引入一种新的,可扩展的算法,用于成本意识的网络拆解.
- 为了动态平衡节点重要性和移除成本,以优化网络中断.
主要方法:
- 开发了成本意识源采样间隔 (CASS-Bet) 算法.
- 基于实时网络变化实现了动态节点优先级.
- 利用可扩展的采样技术来提高计算效率.
主要成果:
- 在社会,基础设施和犯罪网络中,CASS-Bet表现出卓越的表现.
- 该算法使得成本效益高的网络拆解能够在最小的资源开支下实现.
- 在定义移除成本方面实现了高计算效率和灵活性.
结论:
- 对于现实世界的网络拆解挑战,CASS-Bet提供了一种实用且可扩展的解决方案.
- 该算法增强了基础设施的弹性,并有助于破坏有组织犯罪网络.
- 提供了一个灵活的框架,可以适应各种实际成本限制.
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