Mining and generating large-scaled social networks via MapReduce

Yi Chen Lo, Hung Che Lai, Cheng Te Li, Shou De Lin

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


The computational efficiency is usually a concern when dealing with large-scale social network mining tasks containing billions of entities. Cloud computing is widely regarded as a feasible solution to this problem. In this work, we present an open-source graph mining library called the MapReduce Graph Mining Framework (MGMF) to be a robust and efficient MapReduce-based graph mining tool. We start from dividing graph mining algorithms into several categories and design a MapReduce framework for algorithms in each category. The experimental results show that MGMF is 3–20 times more efficient than PEGASUS, a state-of-the-art library for graph mining on MapReduce. Moreover, it provides broader coverage of a variety of graph mining algorithms. Furthermore, we designed a model to generate large-scale social networks capturing the power-law degree distribution property by parallelizing the mechanism of preferential attachment so that it is possible to produce billion-sized scale-free network in minutes. Our implemented open-source library can be downloaded from

Original languageEnglish
Pages (from-to)1449-1469
Number of pages21
JournalSocial Network Analysis and Mining
Issue number4
Publication statusPublished - 2013 Jan 1

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Communication
  • Media Technology
  • Human-Computer Interaction
  • Computer Science Applications


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