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.

• Type: Conference
• Year: 2008
• Where: SDM
• Authors: Hanghang Tong, Spiros Papadimitriou, Philip S. Yu, Christos Faloutsos
• Keywords: Data Mining, Important Authors, Powerful Matrix Tools, SDM 2008, Time-evolving Bipartite Graphs