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A Parallel Computing Method for Community Structure Detection Based on BSP Model

机译:基于BSP模型的社区结构检测并行计算方法

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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.
机译:由于在独立环境中用于社区结构检测的常规算法无法处理节点数超过105的巨型网络,并且广泛使用的MapReduce方法在迭代过程中处理过多的I / O操作存在局限性,提出了一种基于BSP(Bulk Synchronous Parallel)模型的高效并行计算方法,用于检测社团结构。在BSP模型的框架下,快速纽曼方法已改进为多步并行计算。在大型网络中发现社区结构更为有效。为了验证所提方法的性能,在hadoop平台的同一集群上构建了一个Hama平台。并为实验模拟了规模为106的数据集。经证实,所提出的方法不仅能够解决单机计算机常规计算中的内存溢出问题,而且与MapReduce模型相比,能够有效提高性能。该方法在大规模网络中具有很高的实用价值。

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