结合基因遗传和贪婪搜索的布谷鸟社区检测算法 postprint

Abstract: 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.