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PVLDB

2010

2010

Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of similarity search in standard graphs, a fundamental problem for probabilistic graphs is to eﬃciently answer k-nearest neighbor queries (k-NN), which is the problem of computing the k closest nodes to some speciﬁc node. In this paper we introduce a framework for processing k-NN queries in probabilistic graphs. We propose novel distance functions that extend well-known graph concepts, such as shortest paths. In order to compute them in probabilistic graphs, we design algorithms based on sampling. During k-NN query processing we eﬃciently prune the search space using novel techniques. Our experiments indicate that our distance functions outperform previously used alternatives in identifying true neighbors in real-world biological data. We also demonstrate that our algorithms scale for graphs with tens of mill...

Related Content

Added |
30 Jan 2011 |

Updated |
30 Jan 2011 |

Type |
Journal |

Year |
2010 |

Where |
PVLDB |

Authors |
Michalis Potamias, Francesco Bonchi, Aristides Gionis, George Kollios |

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