GRAFT: an approximate graphlet counting algorithm for large graph analysis

Authors:
Mahmudur Rahman;Mansurul Bhuiyan;Mohammad Al Hasan
Affiliations:
Indiana University - Purdue University, Indianapolis, IN, USA;Indiana University - Purdue University, Indianapolis, IN, USA;Indiana University - Purdue University, Indianapolis, IN, USA
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

Graphlet frequency distribution (GFD) is an analysis tool for understanding the variance of local structure in a graph. Many recent works use GFD for comparing, and characterizing real-life networks. However, the main bottleneck for graph analysis using GFD is the excessive computation cost for obtaining the frequency of each of the graphlets in a large network. To overcome this, we propose a simple, yet powerful algorithm, called GRAFT, that obtains the approximate graphlet frequency for all graphlets that have upto 5 vertices. Comparing to an exact counting algorithm, our algorithm achieves a speedup factor between 10 and 100 for a negligible counting error, which is, on average, less than 5%; For example, exact graphlet counting for ca-AstroPh takes approximately 3 days; but, GRAFT runs for 45 minutes to perform the same task with a counting accuracy of 95.6%.