摘要
针对复杂网络中基于中心性指标排序方法无法准确依据信息传播的影响力对网络连边排序的难题,提出了一种结合社团结构挖掘算法和边介数的连边重要性度量方法,该方法首先利用Newman算法挖掘网络社团结构,重新定义网络中社团数量,进一步依据边介数对新社团内部连边进行排序,得到每个社团中连边重要性排序,并设计双层网络连边重要性排序方法,得到对整个网络连边重要性的度量。最后通过仿真,以网络效率为评价指标,将本文方法与最大度方法、最大介数方法和随机选边方法3种方法性能进行对比,证明本文方法性能优于这3种方法,能够更好地定位网络中的重要连边。
To overcome the problem that centrality-based ranking methods cannot accurately sort the edge according to the impact on information transmission,this paper puts forward a method that combines the community structure excavation algorithm and the edge betweenness.The proposed method employs the Newman algorithm to mine the community structure of the network and redefine the number of communities.The edge betweenness of each edge is calculated.Based on the community structure and the betweenness of each edge,the edge sorting method in two-layer network is designed.The network efficiency is used as the evaluation index to evaluate the performance of the proposed method.The performance of the proposed method is compared with that of three other methods:the maximum degree method,the maximum edge betweenness method and the random edge selection method.Simulation results demonstrate that the proposed method performs better than these three methods,better identifying important edges in the network.
作者
罗章凯
裴忠民
王新敏
唐婉莹
LUO Zhangkai;PEI Zhongmin;WANG Xinmin;TANG Wanying(Space Engineering University,Beijing 101416,China)
基金
复杂系统仿真实验室基础研究项目(DXZT-JC-ZZ-2020-003)。
