Nature-inspired optimization Algorithms (NIOAs) are nowadays a popular choice for community detection in social networks. Community detection problem in social network is treated as optimization problem, where the objective is to either maximize the connection within the community or minimize connections between the communities. To apply NIOAs, either of the two, or both objectives are explored. Since NIOAs mostly exploit randomness in their strategies, it is necessary to analyze their performance for specific applications. In this paper, NIOAs are analyzed on the community detection problem. A direct comparison approach is followed to perform pairwise comparison of NIOAs. The performance is measured in terms of five scores designed based on prasatul matrix and also with average isolability. Three widely used real-world social networks and four NIOAs are considered for analyzing the quality of communities generated by NIOAs.
翻译:社会网络中的社区发现问题被视为优化问题,其目标是最大限度地扩大社区内部的联系或最大限度地减少社区之间的联系; 探索采用两个或两个目标中的两个或两个目标,以应用非土著协会; 由于非土著协会在战略中大多利用随机性,有必要分析其具体应用的性能; 本文分析了非土著协会在社区发现问题上的绩效; 采用直接比较方法对非土著协会进行比较; 以基于光学矩阵和平均易腐性设计的五分成绩来衡量绩效; 考虑使用三种广泛使用的实体世界社会网络和四种非土著协会来分析非土著协会产生的社区质量。