文章基本信息

标题：A Parallel Computing Method for Community Structure Detection Based on BSP Model
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作者：Sun, Yi ; Hua, Zhen ; Zou, Lihui 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2014
卷号：9
期号：7
页码：1876-1885
DOI：10.4304/jsw.9.7.1876-1885
语种：English
出版社：Academy Publisher
摘要：Since the conventional algorithm for community structure detection in a stand-alone environment cannot handle the giant network whose number of nodes is more than 10⁵, and the widely used MapReduce method has a limitation on dealing with excessive I/O operations during the iterative process, an efficient parallel computing method based on BSP (Bulk Synchronous Parallel) model for detecting community structure is proposed in this paper. The Fast Newman method is improved into parallel calculations with multiple steps under the framework of BSP model. It is more efficient to discover community structures in the large scale network. In order to testify the performance of the proposed method, a hama platform was built up on the same cluster of the hadoop platform. And a dataset, at a scale of 10⁶, was also simulated for the experiments. It is approved that the proposed method is not only able to solve the issue of memory overrun in the conventional calculation on a stand-alone computer, but also to improve the performance effectively comparing to the MapReduce model. The proposed method has high practical value in large scale networks.
关键词：complex networks;graph clustering;modularity;Fast-Newman algorithm;BSP model