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COUNTING TRIANGLES IN MASSIVE GRAPHS WITH MAPREDUCE (2013)
| Citations: | 12 - 4 self |
BibTeX
@MISC{Kolda13countingtriangles,
author = {Tamara G. Kolda and Ali Pinar and Todd Plantenga and C. Seshadhri and Christine Task},
title = {COUNTING TRIANGLES IN MASSIVE GRAPHS WITH MAPREDUCE },
year = {2013}
}
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Abstract
Graphs and networks are used to model interactions in a variety of contexts. There is a growing need to quickly assess the characteristics of a graph in order to understand its underlying structure. Some of the most useful metrics are triangle-based and give a measure of the connectedness of mutual friends. This is often summarized in terms of clustering coefficients, which measure the likelihood that two neighbors of a node are themselves connected. Computing these measures exactly for large-scale networks is prohibitively expensive in both memory and time. However, a recent wedge sampling algorithm has proved successful in efficiently and accurately estimating clustering coefficients. In this paper, we describe how to implement this approach in MapReduce to deal with extremely massive graphs. We show results on publicly-available networks, the largest of which is 132M nodes and 4.7B edges, as well as artificially generated networks (using the Graph500 benchmark), the largest of which has 240M nodes and 8.5B edges. We can estimate the clustering coefficient by degree bin (e.g., we use exponential binning) and the number of triangles per bin, as well as the global clustering coefficient and total number of triangles, in an average of 0.33 sec. per million edges plus overhead (approximately 225 sec. total for our configuration). The technique can also be used to study triangle statistics such as the ratio of the highest and lowest degree, and we highlight differences between social and non-social networks. To the best of our knowledge, these are the largest triangle-based graph computations published to date.
Keyphrases
counting triangle massive graph mapreduce triangle statistic triangle-based graph computation underlying structure non-social network large-scale network useful metric mutual friend recent wedge publicly-available network massive graph total number degree bin graph500 benchmark exponential binning global clustering coefficient
