Measuring proximity on graphs with side information

Hanghang Tong, Huiming Qu, Hani Jamjoom

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
Pages598-607
Number of pages10
DOIs
StatePublished - Dec 1 2008
Event8th IEEE International Conference on Data Mining, ICDM 2008 - Pisa, Italy
Duration: Dec 15 2008Dec 19 2008

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other8th IEEE International Conference on Data Mining, ICDM 2008
CountryItaly
CityPisa
Period12/15/0812/19/08

ASJC Scopus subject areas

  • Engineering(all)

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