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

• Autores: Charalampos E. Tsourakakis
• Localización: Social network mining, analysis, and research trends: techniques and applications / I-Hsien Ting (ed. lit.), Tzung-Pei Hong (ed. lit.), Leon S.L. Wang (ed. lit.), 2012, ISBN 978-1-61350-513-7, págs. 66-78
• Idioma: inglés
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• Resumen

  • In recent years, a considerable amount of research has focused on the study of graph structures arising from technological, biological and sociological systems. Graphs are the tool of choice in modeling such systems since they are typically described as sets of pairwise interactions. Important examples of such datasets are the Internet, the Web, social networks, and large-scale information networks which reach the planetary scale, e.g., Facebook and Linkedln. The necessity to process large datasets, including graphs, has led to a major shift towards distributed computing and parallel applications, especially in the recent years. MapReduce was developed by Google, one of the largest users of multiple processor computing in the world, for facilitating the development of scalable and fault tolerant applications. MapReduce has become the de facto standard for processing large scale datasets both in industry and academia. 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.edukpegasus/.