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首页> 外文期刊>International Journal of Rough Sets and Date Analysis >Metaheuristic Algorithms for Detect Communities in Social Networks: A Comparative Analysis Study
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Metaheuristic Algorithms for Detect Communities in Social Networks: A Comparative Analysis Study

机译:社交网络中检测社区的元启发式算法:比较分析研究

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>This article presents a comparative analysis between Cuckoo Search Optimization Algorithm, Lion Optimization Algorithm and Ant-Lion Optimization Algorithm. Zachary karate Club, The Bottlenose Dolphin Network, American College Football Network, and Facebook used as benchmark datasets for comparison, the results proved those algorithms can define the structure and detect communities of complex networks with high accuracy and quality based on different method that it used. The Cuckoo Search Optimization Algorithm is the best algorithm compared to Ant-Lion Optimization Algorithm and Lion Optimization Algorithm as it got greatest number of communities, detect communities in used benchmark datasets with average accuracy %69, average modularity %62 and average fitness %60.
机译:>本文介绍了杜鹃搜索优化算法,Lion优化算法和Ant-Lion优化算法之间的比较分析。 Zachary空手道俱乐部,宽吻海豚网络,美国大学橄榄球网络和Facebook用作比较的基准数据集,结果证明这些算法可以根据其使用的不同方法来定义结构并以高精度和高质量检测复杂网络的社区。 。与蚁狮优化算法和狮子优化算法相比,布谷鸟搜索优化算法是最好的算法,因为它具有最多的社区数量,可以在使用的基准数据集中以平均准确度%69,平均模块性%62和平均适应度%60来检测社区。

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