随机模糊算法下的复杂社会网络研究  

Complex Social Networks Based on Random Fuzzy Algorithm

作　　者：俞静[1] 季姝[1] YU Jing;JI Shu(Business School,Hohai University,Nanjing 211100)

机构地区：[1]河海大学商学院,南京211100

出　　处：《计算机与数字工程》2017年第5期940-944,共5页Computer & Digital Engineering

基　　金：国家自然科学基金面上项目(编号:71171207);河海大学中央高校基本科研业务费项目(编号:2013B33114)资助

摘　　要：针对全球化发展带来的复杂社会网络节点关系分析难度大的问题,论文在随机模糊算法概念下对复杂社会网络的节点集合进行划分。利用复杂社会网络拓扑结构性质,从网络矩阵关系结构为出发点,分别修正了网络节点路径长度和边集内聚集系数。通过随机概率定义实现了完全规则网络到完全随机网络的过渡,对每个独立的社会网络节点进行编码并设定唯一的标签,完成网络节点集合的聚集度发现。通过算法仿真结果表明:随机模糊算法在随机概率取0.5时,复杂社会网络节点聚集划分过程中节点时间复杂度优于传统的模糊聚类算法,该算法在8次迭代后社会网络节点重叠模块率达到0.92。Network nodes for complex social relations brought about by globalization analysis difficult problems,the present study in the random algorithm fuzzy concept network node complex set of social divide.The use of complex topologies nature of social networks,from the relationship network matrix structure as a starting point,each network node corrected path length and clustering coefficient within the edge set.By random probability defined rules to achieve a complete network transition to a fully random network,encode each independent social network nodes and assign a unique tag,complete set of network node discovery.By algorithm simulation results show that,the random probability is0.5,and complex social network aggregation node node partitioning of time complexity than traditional fuzzy clustering algorithm after8iterations social network node module overlap rate of0.92.

关 键 词：社会网络 网络节点 随机概率 模糊算法 

分 类 号：TP391.6[自动化与计算机技术—计算机应用技术]