基于模糊均值的细菌群体趋药性复杂网络社团结构发现

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

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.