Abstract: Target control, aiming to study how to choose and control the part of nodes in the network, has mainly adopted random selection and local selection without considering the importance of nodes. Since the node importance evaluation index of existing complex network is single, this paper adopts a node importance evaluation method based on multiple-attribute decision from different angles by using several node importance indices. In order to      select important nodes for the target control, this paper gives different weights to evaluate and rank the nodes. The experimental results show that the method is able to select less driver nodes in both artificial data and real data sets.

Key words: target control, multiple-attribute decision, node importance

摘要： 目标控制，旨在研究如何选择与控制网络中的部分节点，已有工作主要采用随机选取和局部选取来进行，并没有考虑节点的重要性。针对现有的复杂网络节点重要性的评价指标比较单一的问题，在目标控制中采用了一种基于多属性决策的节点重要性综合评价方法，从不同的角度，利用网络中多个节点重要性指标，分别给出不同的权重对节点进行综合评价并且排序，以此选取重要的节点进行目标控制。在人工生成数据及真实数据集上的实验结果表明，该方法能够选出较少的驱动节点。

关键词: 目标控制, 多属性决策, 节点重要性