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Proceedings of the 2009 SIAM International Conference on Data Mining

Randomization Techniques for Graphs


Mining graph data is an active research area. Several data mining methods and algorithms have been proposed to identify structures from graphs; still, the evaluation of those results is lacking. Within the framework of statistical hypothesis testing, we focus in this paper on randomization techniques for unweighted undirected graphs. Randomization is an important approach to assess the statistical significance of data mining results. Given an input graph, our randomization method will sample data from the class of graphs that share certain structural properties with the input graph. Here we describe three alternative algorithms based on local edge swapping and Metropolis sampling. We test our framework with various graph data sets and mining algorithms for two applications, namely graph clustering and frequent subgraph mining.

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cover image Proceedings
Proceedings of the 2009 SIAM International Conference on Data Mining
Pages: 780 - 791
Editors: Chid Apte, IBM T.J. Watson Research Center, Yorktown Heights, New York, Haesun Park, Georgia Institute of Technology, Atlanta, Georgia, Ke Wang, Simon Fraser University, Burnaby, British Columbia, Canada, and Mohammad J. Zaki, Rensselaer Polytechnic Institute, Troy, New York
ISBN (Print): 978-0-898716-82-5
ISBN (Online): 978-1-61197-279-5


Published online: 18 December 2013



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