论文标题
自然风格的优化算法的直接比较分析在社交网络中的社区检测问题
Direct Comparative Analysis of Nature-inspired Optimization Algorithms on Community Detection Problem in Social Networks
论文作者
论文摘要
如今，受自然启发的优化算法（NIOAS）是社交网络中社区发现的流行选择。社交网络中的社区检测问题被视为优化问题，目的是使社区内的联系最大化，或者最小化社区之间的联系。要应用NIOA，探索了两个或两个目标。由于Nioas主要利用其策略中的随机性，因此有必要分析其针对特定应用的绩效。在本文中，对社区检测问题进行了分析。遵循直接比较方法进行NIOAS的成对比较。该性能是根据基于Prasatul矩阵设计的五个分数以及平均隔离性来衡量的。考虑了三个广泛使用的现实世界社交网络和四个NIOA，用于分析NIOAS产生的社区质量。
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.
