摘要： 针对传统智能进化社区发现算法通常存在弱化节点属性和容易过早收敛等问题，提出基于邻边属性群智能聚类的个人社交网络社区发现算法NLA/SCD。在融合邻边结构及其节点属性相似特性的基础上，定义社会蜘蛛优化算法的适应度函数，并将社区模块度增量作为算子迭代准则。在雌性和雄性个体的进化与交配过程中，利用适应度函数和模块度增量函数从局部和全局角度优化社区划分的寻优过程，以保持种群多样性并避免算法过早收敛。实验结果表明，NLA/SCD算法能有效识别属性信息多样的个人社交网络，且具有较高的运行速度和划分精度。

关键词: 个人社交网络, 社区发现, 邻边属性, 社会蜘蛛优化算法, 适应度函数

Abstract: The traditional intelligent evolution community discovery algorithms are usually have the problems such as weakening node attributes and prone to premature convergence.To address the problems,this paper proposes a community discovery algorithm,NLA/SCD,using swarm-intelligence-based clustering of adjacent edge attributes for personal social networks.By fusing the structures of adjacent edges and the similar features of their node attributes,the adaptive function of the Social Spider Optimization(SSO) algorithm is defined,and the increment of the community modularity is selected as the iterative criterion of the operator.Then,as the male and female individuals evolve and mate,the adaptive function and the modularity increment function are used to locally and globally optimize the process of the community division optimization.Experimental results show that the NLA/SCD algorithm can effectively detect the personal social networks with diverse attribute information,and it maintains a high division accuracy while running fast.

Key words: personal social network, community discovery, adjacent edge attribute, Social Spider Optimization(SSO) algorithm, fitness function

中图分类号: 

• TP393