Measuring Structural Similarities of Graphs in Linear Time

  • Zheng Fang
  • You Li
  • Jie Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7402)

Abstract

Measuring graph similarities is an important topic with numerous applications. Early algorithms often incur quadratic time or higher, making it unpractical to use for graphs of very large scales. We present in this paper the first-known linear-time algorithm for solving this problem. Our algorithm, called Random Walker Termination (RWT), is based on random walkers and time series. Three major graph models, that is, the Erdős-Rényi random graphs, the Watts-Strogatz small world graphs, and the Barabási-Albert preferential attachment graphs are used to generate graphs of different sizes. We show that the RWT algorithm performs well for all three graph models. Our experiment results agree with the actual similarities of generated graphs. Built on stochastic process, RWT is sufficiently stable to generate consistent results. We use the graph edge rerouting test and the cross model test to demonstrate that RWT can effectively identify structural similarities between graphs.

Keywords

Graph Model Random Graph Sink Node Preferential Attachment Diffusion Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zheng Fang
    • 1
  • You Li
    • 1
  • Jie Wang
    • 1
  1. 1.Department of Computer ScienceUniversity of MassachusettsLowellUSA

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