基于单目标粒子群算法的社区检测算法

Chao Wang, Jilian Guo, A. Shen, S.-H. Huang
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引用次数: 0

摘要

随着信息技术的发展,复杂网络在人们的生活中越来越普遍,基于模块化优化的方法越来越受到人们的关注。由于传统的粒子群算法是解决连续优化问题,群落结构检测问题是一个基于图的离散优化问题。我们采用了一种新的编码策略和粒子更新策略来克服这个问题。在更新策略中,我们引入了一种基于邻域更新的方法,确保粒子的更新在一定程度上遵循邻域信息,符合真实复杂网络的特点。此外,利用扩展模块密度函数进行优化,克服了传统模块密度函数的分辨率限制问题,确保在不同分辨率下找到复杂网络的群落结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Community Detection Algorithm Based on Single Target PSO
With the development of information technology, complex networks have become more common in people's lives, and methods based on modularity optimization have attracted more and more attention. Because the traditional particle swarm algorithm is used to solve continuous optimization problems, the community structure detection problem is a discrete optimization problem based on graphs. We applied a new coding strategy and a particle update strategy to overcome this problem. In the update strategy, we introduced a method based on neighbor update to ensure that the update of the particles is guided to a certain extent by following the neighborhood information, which is in line with the characteristics of real complex networks. In addition, the expanded module density function is used for optimization to overcome the resolution limitation problem of the traditional module density function and to ensure that the community structure of complex networks is found at different resolutions.
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