Vertex-Cut Partitioning Performance Analysis for FASTCD Algorithm in Large-Scale Graph

Rizki Rusdiwijaya, G. Saptawati
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Abstract

Large-scale graph processing has become a popular research topic in domain graph partitioning and community detection. FastCD is a community-based detection algorithm based on modularity optimization capable of detecting communities on large-scale graphs. Community detection on large graphs requires graph partitioning techniques that partition large-scale graphs into several subgraphs for processing carried out in parallel, so that computational loads can be distributed across machines in the cluster. The research was conducted on graph-parallel distributed framework, GraphX, which is a graph processing component in Spark. The strategies of vertex-cut partitioning method such as RandomVertexCut, CanonicalRandomVertexCut, EdgePartition1D, and EdgePartition2D applied to the FastCD to perform community detection on large scale graphs in parallel. Furthermore, EdgePartition1D has the best performance for FastCD performing parallel community detection on large-scale graphs with the number of edges 7,600,595 and vertices 685,230. The results of this research can be seen that the FastCD algorithm with EdgePartition1D and EdgePartition2D is able to maintain valuable information contained in graphs by considering vertices and neighboring edges quickly.
大规模图中FASTCD算法的点切分割性能分析
大规模图处理已成为领域图划分和社区检测领域的研究热点。FastCD是一种基于模块化优化的社区检测算法,能够检测大规模图上的社区。大型图的社区检测需要图分区技术,该技术将大型图划分为几个子图,以便并行进行处理,以便计算负载可以分布在集群中的各个机器上。本文对Spark中的图形处理组件GraphX进行了图形并行分布式框架的研究。将RandomVertexCut、CanonicalRandomVertexCut、EdgePartition1D、EdgePartition2D等点切分区策略应用于FastCD,实现大规模图并行社区检测。此外,EdgePartition1D对于FastCD在边数为7,600,595、顶点数为685,230的大规模图上执行并行社区检测具有最佳性能。本研究的结果可以看出,采用EdgePartition1D和EdgePartition2D的FastCD算法能够通过快速考虑顶点和相邻边来维护图中包含的有价值的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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