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

Charalampos E. Tsourakakis (Carnegie Mellon University, USA)

Source Title: Social Network Mining, Analysis, and Research Trends: Techniques and Applications

DOI: 10.4018/978-1-61350-513-7.ch005

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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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Background

In this section we provide the necessary background: the MapReduce framework, basic graph theoretic definitions and the Flajolet-Martin method for counting distinct elements in a multiset.

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

Abstract

Background

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