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Efficient algorithms for large-scale local triangle counting

Efficient algorithms for large-scale local triangle counting Ef cient Algorithms for Large-Scale Local Triangle Counting LUCA BECCHETTI œSapienza  Universita di Roma ` PAOLO BOLDI Universita degli Studi di Milano ` and CARLOS CASTILLO and ARISTIDES GIONIS Yahoo! Research, Spain In this article, we study the problem of approximate local triangle counting in large graphs. Namely, given a large graph G = (V, E) we want to estimate as accurately as possible the number of triangles incident to every node v ˆ V in the graph. We consider the question both for undirected and directed graphs. The problem of computing the global number of triangles in a graph has been considered before, but to our knowledge this is the rst contribution that addresses the problem of approximate local triangle counting with a focus on the ef ciency issues arising in massive graphs and that also considers the directed case. The distribution of the local number of triangles and the related local clustering coef cient can be used in many interesting applications. For example, we show that the measures we compute can help detect the presence of spamming activity in largescale Web graphs, as well as to provide useful features for content quality assessment in social http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2010 by ACM Inc.
ISSN
1556-4681
DOI
10.1145/1839490.1839494
Publisher site
See Article on Publisher Site

Abstract

Ef cient Algorithms for Large-Scale Local Triangle Counting LUCA BECCHETTI œSapienza  Universita di Roma ` PAOLO BOLDI Universita degli Studi di Milano ` and CARLOS CASTILLO and ARISTIDES GIONIS Yahoo! Research, Spain In this article, we study the problem of approximate local triangle counting in large graphs. Namely, given a large graph G = (V, E) we want to estimate as accurately as possible the number of triangles incident to every node v ˆ V in the graph. We consider the question both for undirected and directed graphs. The problem of computing the global number of triangles in a graph has been considered before, but to our knowledge this is the rst contribution that addresses the problem of approximate local triangle counting with a focus on the ef ciency issues arising in massive graphs and that also considers the directed case. The distribution of the local number of triangles and the related local clustering coef cient can be used in many interesting applications. For example, we show that the measures we compute can help detect the presence of spamming activity in largescale Web graphs, as well as to provide useful features for content quality assessment in social

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD)Association for Computing Machinery

Published: Oct 1, 2010

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