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A Method Towards Community Detection Based on Estimation of Distribution Algorithm

机译：一种基于分布估计算法的社区检测方法

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摘要

Estimation of Distribution Algorithm (EDA) is a stochastic optimization algorithm based on statistical theory. It has strong global search ability, but it is easy to fall into the local optimal solution and can not get good results in community detection. In order to solve this problem, we propose a community detection algorithm based on Estimation of Distribution Algorithm, named EDACD, whose basic framework refers EDA and the target function is modularity. EDACD keeps population diversity by adding crossover mutation operation of Genetic Algorithm as well as the improvement of probability model. Genetic Algorithm is based on "micro" level of gene, which has good local optimization ability; EDA uses the evolutionary method based on "macro" level of search space, which has strong global search ability and fast convergence speed. Taking advantage of the two methods, EDACD can used to improve the search ability of algorithm from "micro" and "macro" two levels. Finally, by experimenting on some typical real-world networks and computer-generated networks, the experimental results show that the proposed algorithm can detect the community division accurately, and has higher clustering precision compared with some representative algorithms. In addition, the proposed algorithm also has a fast convergence rate.
机译：分布估计算法（EDA）是一种基于统计理论的随机优化算法。它具有强大的全局搜索能力，但很容易陷入局部最优解，在社区检测中无法获得良好的结果。为了解决这个问题，我们提出了一种基于分布估计算法的社区检测算法EDACD，其基本框架是EDA，目标功能是模块化。 EDACD通过添加遗传算法的交叉变异操作以及概率模型的改进来保持种群多样性。遗传算法基于基因的“微观”水平，具有良好的局部优化能力; EDA使用基于“宏观”搜索空间级别的进化方法，具有强大的全局搜索能力和较快的收敛速度。利用这两种方法，EDACD可以从“微观”和“宏观”两个层次提高算法的搜索能力。最后，通过对一些典型的现实世界网络和计算机生成的网络进行实验，实验结果表明，与某些代表性算法相比，该算法可以准确地检测出社区划分，并且具有较高的聚类精度。另外，该算法还具有较快的收敛速度。

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