Proximity tracking on time-evolving bipartite graphs

Hanghang Tong, Spiros Papadimitriout, Philip S. Yu, Christos Faloutsos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

55 Citations (Scopus)

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 languageEnglish (US)
Title of host publicationSociety for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130
Pages704-715
Number of pages12
Volume2
StatePublished - 2008
Externally publishedYes
Event8th SIAM International Conference on Data Mining 2008, Applied Mathematics 130 - Atlanta, GA, United States
Duration: Apr 24 2008Apr 26 2008

Other

Other8th SIAM International Conference on Data Mining 2008, Applied Mathematics 130
CountryUnited States
CityAtlanta, GA
Period4/24/084/26/08

Fingerprint

Collaborative filtering
Bipartite Graph
Proximity
Vertex of a graph
Experiments
Collaborative Filtering
Restart
Centrality
Citations
Social Networks
Mining
Random walk
Monitor
Speedup
Experiment

ASJC Scopus subject areas

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

Cite this

Tong, H., Papadimitriout, S., Yu, P. S., & Faloutsos, C. (2008). Proximity tracking on time-evolving bipartite graphs. In 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.

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tong, H, Papadimitriout, S, Yu, PS & Faloutsos, C 2008, Proximity tracking on time-evolving bipartite graphs. in 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.
Tong H, Papadimitriout S, Yu PS, Faloutsos C. Proximity tracking on time-evolving bipartite graphs. In Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130. Vol. 2. 2008. p. 704-715
Tong, Hanghang ; Papadimitriout, Spiros ; Yu, Philip S. ; Faloutsos, Christos. / Proximity tracking on time-evolving bipartite graphs. Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130. Vol. 2 2008. pp. 704-715
@inproceedings{5241787c53964d729190eadc7ba03e27,
title = "Proximity tracking on time-evolving bipartite graphs",
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.",
author = "Hanghang Tong and Spiros Papadimitriout and Yu, {Philip S.} and Christos Faloutsos",
year = "2008",
language = "English (US)",
isbn = "9781605603179",
volume = "2",
pages = "704--715",
booktitle = "Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130",

}

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 -