基于物理场论的探测复杂网络社团结构的分布估计算法

探测社团结构是复杂网络分析中一个基本和重要的问题。为提高探测社团结构的效率，本文提出了基于复杂网络场论的社团结构分布估计算法。通过设置不同种群规模，本文算法运用经典物理场论理论构建节点间场论模型，并在此基础上建立了社团结构概率模型，按照社团结构概率模型建立了分布估计算法。将该算法与GN(Girvan Newman)算法、遗传算法及启发式算法比较其产生的最优解，并分析它们的均值及方差的差异，结果表明：基于复杂网络场论的社团结构分布估计算法收敛速度较快，划分效果较好。

Estimation of Distribution Algorithm for Detecting Community Structure of Complex Networks Based on Field Theory Model

Identification and detection of the community structure is fundamental and important in the analysis of complex network. To detect community structure precisely, a new community detection algorithm based on EDA (Estimation of distribution algorithms) and field theory is proposed. By studying the instance relation of complex network and introducing the field theory, a community structure probability model is built. The proposed algorithm is illustrated and compared with GN (Girvan Newman) algorithm, genetic algorithm and heuristic algorithm by using classic real world networks. The result demonstrates the proposed algorithm is converge quickly and good practice.