CAJ | 학술논문

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，提出一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。
재분석망락모괴성함수국부단조성적기출상，설계국부수색변이산자，제출일충구해사구검측문제적개진분포고계산법。기우기준측시망락화대규모복잡망락대산법진행측시，실험결과표명，대우불동망락，해산법운행100차득도적Q함수평균치균우우Girvan-Newman산법、Newman쾌속산법화Tasgin유전산법。
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).