局部扩展的遗传优化重叠社区发现方法

重叠社区结构是复杂网络的一种重要的特征，提出了一种局部扩展的遗传优化重叠社区发现（LEGAOCD）。借鉴局部扩展的重叠社区发现方法的思想，将少数的核心节点构成模体；同时，利用了三角形模体来判断社区的稳定性度量问题，从而量化社区结构稳定性；然后通过改进的遗传优化算法策略分配它们应归属的社区；最后通过两个评价目标函数得到高质量的重叠社区结构。该算法在数据集上与经典的CPM算法、COPRA算法作比较，实验结果表明，LEGAOCD算法在检测重叠社区结构和重叠节点方面具有较优的性能。

Local extension approach through genetic algorithm for overlapping community detection

Overlapping community structure is one of the most important features of complex network. This study proposed a local extended genetic algorithm optimization overlapping community detection(LEGAOCD) . It makes a few core nodes be constructed as die body, yet regards the main idea of local extended overlapping community detection as reference; at the same time, this paper uses the triangular model to judge the stability measure of the community, so as to quantify the stability of community structure. Then, the improved strategy of genetic algorithm is used to allocate the communities where they belong. Finally, the high-quality overlapping community structure is obtained by two discriminant objective functions. After that, the LEGAOCD is compared with classical CPM and COPRA algorithms on the data sets, the results show that LEGAOCD possesses excellent comparatively in the aspects of detecting overlapping community structure and overlapping nodes.