期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2017
卷号：7
期号：2
页码：35
DOI：10.5121/ijdkp.2017.7203
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Community detection from complex information networks draws much attention from both academia andindustry since it has many real-world applications. However, scalability of community detection algorithmsover very large networks has been a major challenge. Real-world graph structures are often complicatedaccompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA thatparallelizes a local community identification method which uses the $M$ metric. Then we adopt aniterative expansion approach to find all the communities in the graph. Empirical results show that for largenetworks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditionalsequential approach to detect communities using the M-measure. The result shows that for local communitydetection, when the data is too big for the original M metric-based sequential iterative expension approachto handle, our MapReduce version 3MA can finish in a reasonable time.
关键词：Social Network Analysis; Community Mining; MapReduce
