计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 241-247.doi: 10.11896/jsjkx.201200067
张程瑞, 陈俊杰, 郭浩
ZHANG Cheng-rui, CHEN Jun-jie, GUO Hao
摘要: 鲁棒性作为一种动态行为也是超网络领域的研究热点,对构建鲁棒网络具有重要的现实意义。尽管对超网络的研究越来越多,但对其动态研究相对较少,尤其是在神经影像领域。在现有的脑功能超网络研究中,大多是探究网络的静态拓扑属性,并没有相关研究对脑功能超网络的动力学特性——鲁棒性展开分析。针对这些问题,文中首先引入lasso,group lasso和sparse group lasso方法来求解稀疏线性回归模型以构建超网络;然后基于蓄意攻击中的节点度和节点介数攻击两种实验模型,利用全局效率和最大连通子图相对大小探究脑功能超网络在应对攻击时的节点失效网络的鲁棒性,最后通过实验进行对比分析,以探究更为稳定的网络。实验结果表明,在蓄意攻击模式下,group lasso和sparse group lasso方法构建的超网络的鲁棒性更强一些。同时,综合来看,group lasso方法构建的超网络最稳定。
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