在分析网络模块性函数局部单调性的基础上,设计局部搜索变异算子,提出-种求解社区检测问题的改进分布估计算法.基于基准测试网络和大规模复杂网络对算法进行测试,实验结果表明,对于不同网络,该算法运行100次得到的Q函数平均值均优于Girvan-Newman算法、Newman快速算法和Tasgin遗传算法.%Based on the analysis of local monotonic of modularity function,this paper designs a Local Search and Mutation(LSM)operator,and proposes an improved Estimation of Distribution Algorithm(EDA) for solving community detection problem.The proposed algorithm is tested on basic network and big scale complex network.Experimental results show that the Q function average values of this algorithm while running 100 in different networks times is better than Girvan-Newman(GN) algorithm,Fast Newman(FN) algorithm and Tasgin Genetic Algorithm(TGA).
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