陈国强^*， 王宇平

（  西安电子科技大学计算机学院，西安，710071；  ）

摘要： 通过分析复杂网络重叠社团检测问题,提出了衡量重叠社团划分优劣的评判函数,并基于该函数提出一种基于混沌量子粒子群优化的复杂网络重叠社团检测算法,并证明了该算法以概率1收敛.算法采用量子编码,利用 映射初始化粒子种群,以粒子群速度改变方式更新转角的大小.利用计算机生成的网络和实际网络进行了仿真实验,比较了本文算法与经典算法的性能,结果表明,本文算法所得检测结果具有较高的检测正确率,能更好的反映实际社团划分情况。
关键词： 复杂网络；社团检测；重叠社团；离散粒子群算法法

CHEN Guoqiang^*， WANG Yuping

（  School of Computer Science and Technology, Xidian University, Xi'an 710071；  ）

Abstract： By analyzing overlapping community detection of complex networks, an evaluation function of overlapping community is proposed, and a chaos quantum discrete particle swarm algorithm for detecting overlapping community structure based on the function is presented. Then, the convergence of the algorithm with probability 1 is proved. Quantum coding and logistic mapping are used to initialize particle population. The particle swarm speed changed method is used to update the size of the corner. Finally, the simulation experiments are done to test the algorithm on artificial networks and real networks. The results showed that, compared with classical algorithm, the algorithm has higher detection accuracy, can better reflect the real situation of the community divided. To determine the probe made of amino acids arranged in a linear chain and joined together by peptide bonds between the carboxyl and amino groups of adjacent amino acid residues. The sequence of amino acids in a protein is defined by a gene and encoded in the genetic code. This can happen either before the protein is used in the cell, or as part of control mechanisms.

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