一种用于识别复杂网络节点影响力的平均边-距离贡献度方法

An Average Edge-Distance Contribution Method for Identifying Influential Nodes in Complex Networks

如何准确且有效地识别网络中有影响力的节点一直以来都是一个核心问题.为了评估节点的重要性,一些著名的中心性算法已经被提出并得到了广泛的应用.然而,这些方法仍存在一定的缺点.为了能有效地找出网络中有影响力的节点,该文提出了一种新的方法,它被命名为平均边-距离贡献度方法(average edge-distance contribution,简称为AEDC),此方法测量了每条边对于网络中所有节点对距离之和的贡献程度.针对网络中的每个节点,该文从网络中移除它并利用该节点引起的AEDC的相对变化量来决定此节点的影响力大小.为了验证该文提出的AEDC方法的可行性和有效性,使用易感态-感染态-恢复态(SIR)模型模拟了疾病在4个真实的复杂网络上的传播过程.实验结果表明,在识别节点影响力方面,该文所提出的方法比其他几种基准中心性方法更为准确.

How to accurately and effectively identify the influential nodes in networks has always been a core issue.In order to evaluate the importance of the nodes,some famous centrality measures have been proposed and widely used.However,the existing methods still exist some shortcomings.In this work,a novel method named average edge-distance contribution(AEDC)is proposed to effectively find the influential nodes,which measures the average contribution of each edge to the sum of distances of all node pairs in the network.For each node,we utilize the relative change of AEDC by removing it from the network to determine its influence.For verifying the effectiveness and feasibility of the AEDC method,we simulate the process of disease spreading in four real complex networks with the Susceptible-Infected-Recovered(SIR)model.The experimental results show that our proposed method is more accurate than several benchmark centrality measures in terms of identifying the influential nodes.