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ASUNAM

2010

IEEE

2010

IEEE

Subgraph matching is a key operation on graph data. Social network (SN) providers may want to find all subgraphs within their social network that "match" certain query graph patterns. Unfortunately, subgraph matching is NP-complete, making its application to massive SNs a major challenge. Past work has shown how to implement subgraph matching on a single processor when the graph has 10-25M edges. In this paper, we show how to use cloud computing in conjunction with such existing single processor methods to efficiently match complex subgraphs on graphs as large as 778M edges. A cloud consists of one "master" compute node and k "slave" compute nodes. We first develop a probabilistic method to estimate probabilities that a vertex will be retrieved by a random query and that a pair of vertices will be successively retrieved by a random query. We use these probability estimates to define edge weights in an SN and to compute minimal edge cuts to partition the gr...

Added |
26 Oct 2010 |

Updated |
26 Oct 2010 |

Type |
Conference |

Year |
2010 |

Where |
ASUNAM |

Authors |
Matthias Bröcheler, Andrea Pugliese, V. S. Subrahmanian |

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