Chen Yiping, Paul Gerald, Havlin Shlomo, Liljeros Fredrik, Stanley H Eugene
Center for Polymer Studies, Boston University, Boston, MA 02215, USA.
Phys Rev Lett. 2008 Aug 1;101(5):058701. doi: 10.1103/PhysRevLett.101.058701. Epub 2008 Jul 31.
The problem of finding the best strategy to immunize a population or a computer network with a minimal number of immunization doses is of current interest. It has been accepted that the targeted strategies on most central nodes are most efficient for model and real networks. We present a newly developed graph-partitioning strategy which requires 5% to 50% fewer immunization doses compared to the targeted strategy and achieves the same degree of immunization of the network. We explicitly demonstrate the effectiveness of our proposed strategy on several model networks and also on real networks.
寻找用最少数量的免疫剂量使人群或计算机网络免疫的最佳策略这一问题是当前的研究热点。人们已经认识到，针对大多数中心节点的靶向策略对于模型网络和实际网络最为有效。我们提出了一种新开发的图划分策略，与靶向策略相比，该策略所需的免疫剂量减少了5%至50%，并且能实现相同程度的网络免疫。我们明确展示了我们提出的策略在几个模型网络以及实际网络上的有效性。
