付立东,聂靖靖.基于进化谱分方法的动态社团检测[J].计算机科学,2018,45(2):171-174
基于进化谱分方法的动态社团检测
Dynamic Community Detection Based on Evolutionary Spectral Method
投稿时间:2017-05-15  修订日期:2017-08-09
DOI:10.11896/j.issn.1002-137X.2018.02.030
中文关键词:  动态网络,社团结构,模块密度,否定平均关联,进化谱分
英文关键词:Dynamic network,Community structure,Module density,Negative average correlation,Evolution spectrum
基金项目:本文受国家自然科学基金(61502363),陕西省教育厅科学研究计划重点项目(16JZ040)资助
作者单位E-mail
付立东 西安科技大学计算机学院 西安710054 fulidong2005@163.com 
聂靖靖 西安科技大学计算机学院 西安710054  
摘要点击次数: 463
全文下载次数: 301
中文摘要:
      为了有效地分析动态网络中的社团结构功能和特性,在进化时间平滑框架下基于进化聚类方法对模块密度函数和否定平均关联函数进行了优化,论证了理论可行性;在此基础上提出了检测动态网络社团结构的进化谱分算法,并对两类算法进行了详细的谱分分析。分别在计算机合成的动态网络以及真实网络中检验了所提算法的准确性和有效性,并将其与其他算法进行对比。实验结果表明,所提算法对动态网络中的社团检测仍有很高的准确性和有效性。
英文摘要:
      In order to effectively analyze the function and characteristics of the community structure in the dynamic network,the module density function and the negative average correlation function were optimized based on the evolutionaryclustering algorithm under the evolutionary time smoothing framework,and the theoretical feasibility was demonstrated.The evolution spectrum algorithm was proposed based on community structure of the dynamic network.The accuracy and effectiveness of the proposed algorithm was verified and compared with other algorithms in the computer synthesis and real dynamic network respectively.The experimental results show that the proposed algorithm is still very accurate and effective in the community detection of dynamic network.
查看全文  查看/发表评论  下载PDF阅读器