结合基因遗传和贪婪搜索的布谷鸟社区检测算法

为了提高复杂网络社区结构挖掘的精度，结合基因遗传和贪婪搜索提出一种面向模块度优化的布谷鸟社区检测算法(GGCSCA)。布谷鸟种群在有序邻居表上逐维随机游走，并采用优质基因遗传策略，使得种群高效优化，同时应用局部模块度增量最大化的贪婪偏好搜索算法快速提升种群质量，以取得好的社区划分结果。GGCSCA在基准网络和经典网络上进行了实验，并与一些典型算法进行对比，结果说明了本社区发现算法的有效性、准确性和快速收敛性，具有较强的社区识别能力，能够精细地检测出网络社区结构。

Cuckoo search algorithm combining gene inheritance and greedy search for community detection

In order to improve the accuracy of community detection for complex networks, this paper proposed an algorithm based on cuckoo search algorithm combining gene inheritance and greedy search (GGCSCA) to optimize modularity for community detection. Cuckoos walked randomly on ordered adjacent table and employed gene inheritance strategy, which aim to optimize population efficiently. The algorithm improved population quality quickly by greedy preference search of local modularity increment maximum for the purpose of getting good result of community partition. GGCSCA has been tested on both benchmark networks and some typical complex networks, and compared with some typical community detection algorithms. Experimental results show the effectiveness, accuracy and fast convergence of this algorithm for discovering community structure. It has strong capability of community identification and can detect the structure of community finely.