Large Scale Graph Mining with MapReduce: Diameter Estimation and Eccentricity Plots of Massive Graphs with Mining Applications

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Author(s): Charalampos E. Tsourakakis (Carnegie Mellon University, USA)
Copyright: 2012
Pages: 13
Source title: Social Network Mining, Analysis, and Research Trends: Techniques and Applications
Source Author(s)/Editor(s): I-Hsien Ting (National University of Kaohsiung, Taiwan), Tzung-Pei Hong (National University of Kaohsiung, Taiwan)and Leon Shyue-Liang Wang (National University of Kaohsiung, Taiwan)
DOI: 10.4018/978-1-61350-513-7.ch005

Keywords: Data Mining / Information Science Reference / Library & Information Science / Social Computing

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Abstract

In this chapter, the authors present state of the art work on large scale graph mining using MapReduce. They survey research work on an important graph mining problem, estimating the diameter of a graph and the eccentricities/radii of its vertices. Thanks to the algorithm they present in the following, the authors are able to mine graphs with billions of edges, and thus extract surprising patterns. The source code is publicly available at the URL http://www.cs.cmu.edu/~pegasus/.

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Large Scale Graph Mining with MapReduce: Diameter Estimation and Eccentricity Plots of Massive Graphs with Mining Applications

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