### Abstract

Given an author-conference network that evolves over time, which are the conferences that a given author is most closely related with, and how do they change over time? Large time-evolving bipartite graphs appear in many settings, such as social networks, co-citations, market-basket analysis, and collaborative filtering. Our goal is to monitor (i) the centrality of an individual node (e.g., who are the most important authors?); and (ii) the proximity of two nodes or sets of nodes (e.g., who are the most important authors with respect to a particular conference?) Moreover, we want to do this efficiently and incrementally, and to provide "any-time" answers. We propose pTrack and cTrack, which are based on random walk with restart, and use powerful matrix tools. Experiments on real data show that our methods are effective and efficient: the mining results agree with intuition; and we achieve up to 15-176 times speed-up, without any quality loss.

Original language | English (US) |
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Title of host publication | Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130 |

Pages | 704-715 |

Number of pages | 12 |

Volume | 2 |

State | Published - 2008 |

Externally published | Yes |

Event | 8th SIAM International Conference on Data Mining 2008, Applied Mathematics 130 - Atlanta, GA, United States Duration: Apr 24 2008 → Apr 26 2008 |

### Other

Other | 8th SIAM International Conference on Data Mining 2008, Applied Mathematics 130 |
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Country | United States |

City | Atlanta, GA |

Period | 4/24/08 → 4/26/08 |

### Fingerprint

### ASJC Scopus subject areas

- Information Systems
- Software
- Signal Processing
- Theoretical Computer Science

### Cite this

*Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130*(Vol. 2, pp. 704-715)

**Proximity tracking on time-evolving bipartite graphs.** / Tong, Hanghang; Papadimitriout, Spiros; Yu, Philip S.; Faloutsos, Christos.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130.*vol. 2, pp. 704-715, 8th SIAM International Conference on Data Mining 2008, Applied Mathematics 130, Atlanta, GA, United States, 4/24/08.

}

TY - GEN

T1 - Proximity tracking on time-evolving bipartite graphs

AU - Tong, Hanghang

AU - Papadimitriout, Spiros

AU - Yu, Philip S.

AU - Faloutsos, Christos

PY - 2008

Y1 - 2008

N2 - Given an author-conference network that evolves over time, which are the conferences that a given author is most closely related with, and how do they change over time? Large time-evolving bipartite graphs appear in many settings, such as social networks, co-citations, market-basket analysis, and collaborative filtering. Our goal is to monitor (i) the centrality of an individual node (e.g., who are the most important authors?); and (ii) the proximity of two nodes or sets of nodes (e.g., who are the most important authors with respect to a particular conference?) Moreover, we want to do this efficiently and incrementally, and to provide "any-time" answers. We propose pTrack and cTrack, which are based on random walk with restart, and use powerful matrix tools. Experiments on real data show that our methods are effective and efficient: the mining results agree with intuition; and we achieve up to 15-176 times speed-up, without any quality loss.

AB - Given an author-conference network that evolves over time, which are the conferences that a given author is most closely related with, and how do they change over time? Large time-evolving bipartite graphs appear in many settings, such as social networks, co-citations, market-basket analysis, and collaborative filtering. Our goal is to monitor (i) the centrality of an individual node (e.g., who are the most important authors?); and (ii) the proximity of two nodes or sets of nodes (e.g., who are the most important authors with respect to a particular conference?) Moreover, we want to do this efficiently and incrementally, and to provide "any-time" answers. We propose pTrack and cTrack, which are based on random walk with restart, and use powerful matrix tools. Experiments on real data show that our methods are effective and efficient: the mining results agree with intuition; and we achieve up to 15-176 times speed-up, without any quality loss.

UR - http://www.scopus.com/inward/record.url?scp=52649167294&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=52649167294&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781605603179

VL - 2

SP - 704

EP - 715

BT - Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130

ER -