TY - GEN
T1 - Measuring proximity on graphs with side information
AU - Tong, Hanghang
AU - Qu, Huiming
AU - Jamjoom, Hani
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - This paper studies how to incorporate side information (such as users' feedback) in measuring node proximity on large graphs. Our method (ProSIN) is motivated by the well-studied random walk with restart (RWR). The basic idea behind ProSIN is to leverage side information to refine the graph structure so that the random walk is biased towards/away from some specific zones on the graph. Our case studies demonstrate that ProSIN is well-suited in a variety of applications, including neighborhood search, center-piece subgraphs, and image caption. Given the potential computational complexity of ProSIN, we also propose a fast algorithm (Fast-ProSIN) that exploits the smoothness of the graph structures with/without side information. Our experimental evaluation shows that Fast-ProSIN achieves significant speedups (up to 49x) over straightforward implementations.
AB - This paper studies how to incorporate side information (such as users' feedback) in measuring node proximity on large graphs. Our method (ProSIN) is motivated by the well-studied random walk with restart (RWR). The basic idea behind ProSIN is to leverage side information to refine the graph structure so that the random walk is biased towards/away from some specific zones on the graph. Our case studies demonstrate that ProSIN is well-suited in a variety of applications, including neighborhood search, center-piece subgraphs, and image caption. Given the potential computational complexity of ProSIN, we also propose a fast algorithm (Fast-ProSIN) that exploits the smoothness of the graph structures with/without side information. Our experimental evaluation shows that Fast-ProSIN achieves significant speedups (up to 49x) over straightforward implementations.
UR - http://www.scopus.com/inward/record.url?scp=67049167774&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67049167774&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2008.42
DO - 10.1109/ICDM.2008.42
M3 - Conference contribution
AN - SCOPUS:67049167774
SN - 9780769535029
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 598
EP - 607
BT - Proceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
T2 - 8th IEEE International Conference on Data Mining, ICDM 2008
Y2 - 15 December 2008 through 19 December 2008
ER -