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WWW

2011

ACM

2011

ACM

The clustering coeﬃcient of a node in a social network is a fundamental measure that quantiﬁes how tightly-knit the community is around the node. Its computation can be reduced to counting the number of triangles incident on the particular node in the network. In case the graph is too big to ﬁt into memory, this is a non-trivial task, and previous researchers showed how to estimate the clustering coeﬃcient in this scenario. A diﬀerent avenue of research is to to perform the computation in parallel, spreading it across many machines. In recent years MapReduce has emerged as a de facto programming paradigm for parallel computation on massive data sets. The main focus of this work is to give MapReduce algorithms for counting triangles which we use to compute clustering coeﬃcients. Our contributions are twofold. First, we describe a sequential triangle counting algorithm and show how to adapt it to the MapReduce setting. This algorithm achieves a factor of 10-100 speed up over...

Added |
15 May 2011 |

Updated |
15 May 2011 |

Type |
Journal |

Year |
2011 |

Where |
WWW |

Authors |
Siddharth Suri, Sergei Vassilvitskii |

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