基于模糊均值的细菌群体趋药性复杂网络社团结构发现  

Identification of Community Structure in Complex Networks Using Bacterial Colony Chemotaxis with Fuzzy Means Algorithm

作　　者：李艳灵[1,2] 刘先省[1] 

机构地区：[1]河南大学环境规划学院,河南开封475004 [2]信阳师范学院计算机与信息技术学院,河南信阳464000

出　　处：《信阳师范学院学报（自然科学版）》2015年第4期601-603,共3页Journal of Xinyang Normal University(Natural Science Edition)

基　　金：国家自然科学基金项目(61202194);河南省科技计划项目(142400411225)

摘　　要：复杂网络的社团发现问题是网络数据挖掘中的重要问题之一.利用基于模糊C均值的细菌群体趋药性算法最大化网络的模块度,算法中模糊C均值的初始值由群体细菌取药性算法获得.模糊C均值算法在此基础上发现复杂网络的社团结构.其创新点在于最佳模块度的寻找.实验结果表明:该算法具有对现实世界网络社团划分的可行性和有效性.Identification of communities in a complex network is one of the important problems in data mining of network data.The bacterial colony chemotaxis（BCC）strategy with fuzzy C-means（FCM）algorithm was used to maximize the modularity of a network.In the new algorithm,the initial cluster center of FCM algorithm was obtained by BCC algorithm.Then,the FCM algorithm was used for detecting communities in a complex network.The proposed algorithm outperformed most the existing methods in the literature as regards the optimal modularity found.Experimental results for real-word networks confirmed the feasibility and effectiveness of the proposed algorithm.

关 键 词：复杂网络 社团结构 模块度 细菌群体趋药性 模糊C均值 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]