Abstract
Large bipartite graphs which evolve and grow over time (e.g., new links arrive, old links die out, or link weights change) arise 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. In this chapter we propose pTrack, which is based on random walks with restart, together with some important modifications to adapt these measures to a dynamic, evolving setting. Additionally, we develop techniques for fast, incremental updates of these measures that allow us to track them continuously, as link updates arrive. In addition, we discuss variants of our method that can handle batch updates, as well as place more emphasis on recent links. Based on proximity tracking, we further proposed cTrack, which enables us to track the centrality of the nodes over time. We demonstrate the effectiveness and efficiency of our methods on several real data sets.
| Original language | English (US) |
|---|---|
| Title of host publication | Link Mining: Models, Algorithms, and Applications |
| Publisher | Springer New York |
| Pages | 211-236 |
| Number of pages | 26 |
| ISBN (Print) | 9781441965158, 9781441965141 |
| DOIs | |
| State | Published - 2010 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Medicine(all)
Cite this
Proximity tracking on dynamic bipartite graphs : Problem definitions and fast solutions. / Tong, Hanghang; Papadimitriou, Spiros; Yu, Philip S.; Faloutsos, Christos.
Link Mining: Models, Algorithms, and Applications. Springer New York, 2010. p. 211-236.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Proximity tracking on dynamic bipartite graphs
T2 - Problem definitions and fast solutions
AU - Tong, Hanghang
AU - Papadimitriou, Spiros
AU - Yu, Philip S.
AU - Faloutsos, Christos
PY - 2010
Y1 - 2010
N2 - Large bipartite graphs which evolve and grow over time (e.g., new links arrive, old links die out, or link weights change) arise 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. In this chapter we propose pTrack, which is based on random walks with restart, together with some important modifications to adapt these measures to a dynamic, evolving setting. Additionally, we develop techniques for fast, incremental updates of these measures that allow us to track them continuously, as link updates arrive. In addition, we discuss variants of our method that can handle batch updates, as well as place more emphasis on recent links. Based on proximity tracking, we further proposed cTrack, which enables us to track the centrality of the nodes over time. We demonstrate the effectiveness and efficiency of our methods on several real data sets.
AB - Large bipartite graphs which evolve and grow over time (e.g., new links arrive, old links die out, or link weights change) arise 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. In this chapter we propose pTrack, which is based on random walks with restart, together with some important modifications to adapt these measures to a dynamic, evolving setting. Additionally, we develop techniques for fast, incremental updates of these measures that allow us to track them continuously, as link updates arrive. In addition, we discuss variants of our method that can handle batch updates, as well as place more emphasis on recent links. Based on proximity tracking, we further proposed cTrack, which enables us to track the centrality of the nodes over time. We demonstrate the effectiveness and efficiency of our methods on several real data sets.
UR - http://www.scopus.com/inward/record.url?scp=84919871810&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84919871810&partnerID=8YFLogxK
U2 - 10.1007/978-1-4419-6515-8-8
DO - 10.1007/978-1-4419-6515-8-8
M3 - Chapter
AN - SCOPUS:84919871810
SN - 9781441965158
SN - 9781441965141
SP - 211
EP - 236
BT - Link Mining: Models, Algorithms, and Applications
PB - Springer New York
ER -