A voting mechanism-based approach for identifying key nodes in complex networks

Jun Chen, Xuesong Jiang, Xiumei Wei, Yihong Li
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引用次数: 1

Abstract

Many mechanisms, such as epidemic spread, rumor spread, and the spread of social emergencies, are closely related to complex network dynamics, and mining their key nodes plays an important role in understanding the structure and function of the network and maintaining its stable operation. In response to the problem that the key node identification methods in complex networks cannot comprehensively consider global and local information and ignore low-degree nodes, this study proposes a new method based on the voting mechanism. Firstly, the CI value of the network nodes is calculated using the CI algorithm, and initialized the voting ability of nodes by CI values, fully considering the local information of the nodes as well as the influence of low-degree nodes. Secondly, the concept of voting probability is introduced to distinguish the votes of network nodes for their different neighboring nodes through the voting probability, to consider more local information, and to comprehensively assess the importance of the nodes, and ultimately, it is more important to get nodes with the larger voting score. Comparing several classical key node identification methods, the experimental results show that this method can effectively identify key nodes and has a high accuracy rate in different complex networks.
基于投票机制的复杂网络关键节点识别方法
疫情传播、谣言传播、社会突发事件传播等诸多机制都与复杂的网络动态密切相关,挖掘其关键节点对于认识网络结构和功能,维护网络稳定运行具有重要作用。针对复杂网络中关键节点识别方法不能综合考虑全局和局部信息,忽略低度节点的问题,本文提出了一种基于投票机制的关键节点识别方法。首先,利用CI算法计算网络节点的CI值,充分考虑节点的局部信息以及低度节点的影响,通过CI值初始化节点的投票能力;其次,引入投票概率的概念,通过投票概率来区分网络节点对不同相邻节点的投票,考虑更多的局部信息,综合评估节点的重要性,最终更重要的是获得投票得分较大的节点。对比几种经典关键节点识别方法,实验结果表明,该方法在不同复杂网络中都能有效识别关键节点,具有较高的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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