An implementation framework of mapreduce email social network analysis

Rung-Hung Gau*, Tzu Chiang Hsieh, Sheng Wen Tsai, Ching Pei Cheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper, we introduce our own implementation of MapReduce graph-theoretic algorithms for Email social network analysis on the Hadoop platform. Graph theory is a powerful tool for social network analysis and MapReduce is a well-known paradigm for distributed parallel computing. However, based on our own experience, unlike writing conventional Java/C++ programs, writing Java programs to implement MapReduce graph-theoretic algorithms is not straight-forward, even for some fundamental graph-theoretic algorithms. In this paper, for the problem of Email social network analysis, we compare the performance of cloud computing programs with that of conventional computer programs. We show that as long as the size of the input data exceeds a threshold, the cloud computing programs outperform their conventional counterparts.

Original languageEnglish
Title of host publicationWMuNeP'11 - Proceedings of the 7th ACM Workshop on Wireless Multimedia Networking and Computing, Co-located with MSWiM'11
Pages67-69
Number of pages3
DOIs
StatePublished - 13 Dec 2011
Event7th ACM Workshop on Wireless Multimedia Networking and Computing, WMuNeP'11, Co-located with MSWiM'11 - Miami, FL, United States
Duration: 31 Oct 201131 Oct 2011

Publication series

NameWMuNeP'11 - Proceedings of the 7th ACM Workshop on Wireless Multimedia Networking and Computing, Co-located with MSWiM'11

Conference

Conference7th ACM Workshop on Wireless Multimedia Networking and Computing, WMuNeP'11, Co-located with MSWiM'11
CountryUnited States
CityMiami, FL
Period31/10/1131/10/11

Keywords

  • Cloud computing
  • Graph theory
  • MapReduce
  • Social network analysis

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