基于网络透的大型复杂网络的扩散源推理
IEEE transactions on neural networks and learning systems
|October 13, 2023
概括
这项研究优化了扩散源推理 (DSI) 的观察者集,以精确地准信息或疾病起源. 新的基于透的进化框架 (PrEF) 最小化了候选集,以更有效地识别源.
科学领域:
- 网络科学 网络科学
- 信息科学 信息科学 信息科学
- 计算社会科学 计算社会科学
背景情况:
- 扩散源推理 (DSI) 问题对于打击错误信息和控制疾病传播等应用至关重要.
- 优化DSI需要精确的源定位,但在异质网络中,传统的指标失败了.
研究的目的:
- 开发一种优化观察者集配置以提高DSI准确性的方法.
- 为了引入一个新的指标,候选人设置,用于评估DSI的有效性.
- 提出一个框架来分析大规模网络中的DSI.
主要方法:
- 提出了基于透的进化框架 (PrEF),以优化观察者集.
- 引入了候选集指标,以最大组件覆盖面为界限.
- 利用网络透和进化算法,与网络免疫形成并行.
主要成果:
- 与各种网络中的最先进的方法相比,PrEF显著减少了候选集.
- 该方法在26/27实证网络和155/162个临界值病例中表现出卓越的性能.
- 该方法在各种感染概率,扩散模型和网络结构中是稳健的.
结论:
- 预测框架为DSI问题提供了更有效,更稳定的解决方案.
- 这项工作为分析和改进复杂网络中源推理提供了有价值的工具.
- 拟议的候选集指标提供了对DSI方法的更一般的评估.
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