Cheetah: Fast graph kernel tracking on dynamic graphs

Liangyue Li, Hanghang Tong, Yanghua Xiao, Wei Fan

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

10 Scopus citations

Abstract

Graph kernels provide an expressive approach to measuring the similarity of two graphs, and are key building blocks behind many real-world applications, such as bioinformatics, brain science and social networks. However, current methods for computing graph kernels assume the input graphs are static, which is often not the case in reality. It is highly desirable to track the graph kernels on dynamic graphs evolving over time in a timely manner. In this paper, we propose a family of Cheetah algorithms to deal with the challenge. Cheetah leverages the low rank structure of graph updates and incrementally updates the eigen-decomposition or SVD of the adjacency matrices of graphs. Experimental evaluations on real world graphs validate our algorithms (1) are significantly faster than alternatives with high accuracy and (b) scale sub-linearly.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2015, SDM 2015
EditorsSuresh Venkatasubramanian, Jieping Ye
PublisherSociety for Industrial and Applied Mathematics Publications
Pages280-288
Number of pages9
ISBN (Electronic)9781510811522
DOIs
StatePublished - 2015
EventSIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada
Duration: Apr 30 2015May 2 2015

Publication series

NameSIAM International Conference on Data Mining 2015, SDM 2015

Other

OtherSIAM International Conference on Data Mining 2015, SDM 2015
Country/TerritoryCanada
CityVancouver
Period4/30/155/2/15

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

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