BOOK-CHAPTER

Large Scale Graph Mining with MapReduce

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/.

Keywords:
Computer science Graph Data mining Theoretical computer science Data science

Metrics

1
Cited By
0.00
FWCI (Field Weighted Citation Impact)
22
Refs
0.31
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Graph Theory and Algorithms
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Data Mining Algorithms and Applications
Physical Sciences →  Computer Science →  Information Systems
Data Management and Algorithms
Physical Sciences →  Computer Science →  Signal Processing
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