TY - GEN

T1 - Proximity tracking on time-evolving bipartite graphs

AU - Tong, Hanghang

AU - Papadimitriout, Spiros

AU - Yu, Philip S.

AU - Faloutsos, Christos

N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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

U2 - 10.1137/1.9781611972788.64

DO - 10.1137/1.9781611972788.64

M3 - Conference contribution

AN - SCOPUS:52649167294

SN - 9781605603179

T3 - Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130

SP - 704

EP - 715

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

PB - Society for Industrial and Applied Mathematics Publications

T2 - 8th SIAM International Conference on Data Mining 2008, Applied Mathematics 130

Y2 - 24 April 2008 through 26 April 2008

ER -