发表机构
Data Insights AI; School of Computer Science, University of St Andrews（数据洞察人工智能; 圣安德鲁斯大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出消息传递方法研究演化网络年龄定向鲁棒性，发现增长时移除最老顶点加速碎片化，平衡更替时巨分量对年龄定向攻击不变。
AI 中文摘要
我们提出了一种消息传递形式体系，用于研究在顶点添加与删除下演化的网络的年龄定向鲁棒性。我们表明，在增长机制中，移除最老顶点比随机失效更早地使网络碎片化，而最老核心在针对年轻顶点的定向移除下则更为稳健。在顶点平衡更替时，攻击值完全消失，我们证明了巨分量在任意年龄定向下保持不变。
英文摘要
We present a message passing formalism to study the age-targeted robustness of networks that have evolved under the addition and deletion of vertices. We show that in the growing regime removing the oldest vertices fragments the network far sooner than random failure, while the oldest core is far more robust under targeted removal of the young. At a balanced turnover of vertices the attack value vanishes identically and we prove that the giant component is invariant under arbitrary age-targeting.
Comments5 pages, 2 figures
