一种新的网络传播中最有影响力的节点发现方法

在复杂网络的传播模型研究中, 如何发现最具影响力的传播节点在理论和现实应用中都有重大的意义. 目前的研究一般使用节点的度数、紧密度、介数和K-shell等中心化指标来评价影响力, 这种方法虽然简单, 但是由于它们仅利用了节点自身的内部属性, 因而在评价影响力时精确度并不高, 普遍性适用性较弱.为了解决这个问题, 本文提出了KSC (K-shell and community centrality)指标模型. 此模型不但考虑了节点的内部属性, 而且还综合考虑了节点的外部属性, 例如节点所属的社区等. 然后利用SIR (susceptible-infected-recovered)模型对传播过程进行仿真, 实验证明所提出的方法可以更好地发现最具有影响力的节点, 且可适用于各种复杂网络. 本文为这项具有挑战性研究提供了新的思想和方法. 关键词： 复杂网络 最具影响力的节点 社区划分 中性化测量

A new approach to identify influential spreaders in complex networks

In the research of the propagation model of complex network, it is of theoretical and practical significance to detect the most influential nodes. Global metrics such as degree centrality, closeness centrality, betweenness centrality and K-shell centrality can be used to identify the influential spreaders. Each of these approaches is simple but has a low accuracy. We propose K-shell and community centrality (KSC) model. This model considers not only the internal properties of nodes but also the external properties, such as the community which these nodes belong to. The susceptible-infected-recovered model is used to evaluate the performance of KSC model. The experimental result shows that our method is better to detect the most influential nodes. This paper comes up with a new idea and method for the study in this field.
Keywords： complex networks influential node community detecting centrality