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Randomization Techniques for Statistical Significance Testing on Graphs

By Sami Hanhijärvi, Gemma C. Garriga and Kai Puolamäki

Abstract

Studying the patterns and properties of graph data is important in many application areas. A crucial question remains still largely ignored: how significant are the data mining results found on the graph data? Currently, the results are mostly justified by the optimal or near optimal value of the de ned objective function. We study randomization techniques for testing the statistical significance of graph analysis results

Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.178.4682
Provided by: CiteSeerX
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