基于聚类的复杂网络社团结构发现

项目名称： 基于聚类的复杂网络社团结构发现

项目编号： No.61202194

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 计算机科学学科

项目作者： 李艳灵

作者单位： 信阳师范学院

项目金额： 25万元

中文摘要： 针对重叠结构社团发现已成为近几年研究的热点和进行复杂网络社团发现需要设置社团个数的问题，提出基于聚类的复杂网络社团发现算法。将模糊C均值聚类算法用于复杂网络中社团结构的发现，用于发现复杂网络中重叠的社团结构。将智能群算法与模糊C均值算法结合，形成基于智能群算法的模糊C均值社团发现算法，解决模糊C均值算法易陷入局部极值和需要设置初始聚类中心的问题。将空间约束信息加入到传统的模糊C均值算法，通过相似性度量方法的改进提高模糊C均值算法用于社团发现的准确性。通过修正基于空间约束的模糊聚类社团发现算法中隶属度函数的值，加快基于空间约束的模糊聚类社团发现方法的收敛速度。利用均值漂移算法进行社团发现，解决社团个数的设置问题，进一步提高社团发现的准确度。最后将所提算法用于突发群体性事件网络舆情信息传播的预测，通过复杂网络社团结构的发现观察突发性群体事件舆情信息传播的变化情况，从而有效地控制舆情信息的传播。

中文关键词： 复杂网络；社团结构发现；聚类；智能优化算法；均值漂移

英文摘要： Community structure detecting of complex networks based on clustering is proposed because number of community is need to be set in advance and overlapping community structure detection has become research focus in recent years. Fuzzy c-means clustering algorithm is used for community structure detecting in complex network, in which the overlapping community structure is found. The swarm intelligence based fuzzy c-mean algorithm for community detecting is formed by combining swarm intelligence algorithm and fuzzy c-mean algorithm, which avoids the problem of sinking into local extreme. Moreover, the problem of setting initial clustering center is resolved by this algorithm. Space constraints information is added to the traditional fuzzy c-means algorithm and the accuracy of community detecting is enhanced by improving the similarity measure method. Speed of convergence of fuzzy c-means algorithm based on space constraints for community detecting is accelerated by amending the membership function. Mean shift algorithm is used for community detecting, in which the problem of setting the number of community is resolved. Moreover, the accuracy of community detecting is further improved. Finally, the proposed algorithms are used to predict public opinion information network transmission of mass emergency, in which the

英文关键词： complex networks；community structure detecting；clustering；intelligent optimization algorithm；mean shift