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Proximity Tracking on Time-Evolving Bipartite Graphs

9 years 17 days ago
Proximity Tracking on Time-Evolving Bipartite Graphs
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 15176 times speed-up, without any quality loss.
Hanghang Tong, Spiros Papadimitriou, Philip S. Yu,
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where SDM
Authors Hanghang Tong, Spiros Papadimitriou, Philip S. Yu, Christos Faloutsos
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