应用人工鱼群算法的重叠社区检测

Application of artificial fish-school algorithm in overlapping community detection

随着小企业大数据现象的涌现,复杂网络作为复杂系统的建模已很普遍,其中的社区检测是最重要的问题之一。大部分已有的社区检测算法是在社区不重叠情况下进行的,针对现实世界中重叠社区普通存在的现象,提出了一种基于人工鱼群算法的重叠社区检测算法—AFSCDA,初始种群时用标签传播算法对每条人工鱼的寻优变量编码进行调整,避免了非法社区的产生,用模块度Q函数的变形作为适应度函数,来衡量划分的重叠社区质量。在三种经典的已知社区结构的数据集上的测试表明,该算法不仅有效,而且有较高的准确率,能够快速地检测出网络中潜在的社区结构。

With the phenomenon of small business big data emerged,＂complex networks as complex system model＂ has been very popular.Community detection is one of the most important issues.But the existing community detection algorithms mostly assume that no overlaps exist.Aimed at the common phenomenon of overlapping community,an overlapping community detection algorithm,named AFSCDA,is proposed based on fish-school algorithm.In the initialization phase,a label propagation algorithm is utilized on optimization variables of each artificial fish for coding adjustment,trying to avoid illegal community.We will apply the deformation module of the Q function as the fitness function.In experiments,the algorithm is applied to three classic datasets with known community structures in order to demonstrate the algorithm＇s effectiveness,higher accuracy,capability of detecting the potential community structure quickly in networks.