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Large Scale Graph Mining with MapReduce: Counting Triangles in Large Real Networks

Large Scale Graph Mining with MapReduce: Counting Triangles in Large Real Networks

Charalampos E. Tsourakakis
Copyright: © 2012 |Pages: 16
ISBN13: 9781613500538|ISBN10: 161350053X|EISBN13: 9781613500545
DOI: 10.4018/978-1-61350-053-8.ch013
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MLA

Tsourakakis, Charalampos E. "Large Scale Graph Mining with MapReduce: Counting Triangles in Large Real Networks." Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, IGI Global, 2012, pp. 299-314. https://doi.org/10.4018/978-1-61350-053-8.ch013

APA

Tsourakakis, C. E. (2012). Large Scale Graph Mining with MapReduce: Counting Triangles in Large Real Networks. In S. Sakr & E. Pardede (Eds.), Graph Data Management: Techniques and Applications (pp. 299-314). IGI Global. https://doi.org/10.4018/978-1-61350-053-8.ch013

Chicago

Tsourakakis, Charalampos E. "Large Scale Graph Mining with MapReduce: Counting Triangles in Large Real Networks." In Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, 299-314. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-053-8.ch013

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Abstract

In this Chapter, we present state of the art work on large scale graph mining using MapReduce. We survey research work on an important graph mining problem, counting the number of triangles in large-real world networks. We present the most important applications related to the count of triangles and two families of algorithms, a spectral and a combinatorial one, which solve the problem efficiently.

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