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Lecture Notes in Computer Science
Clustering graphs based on a comparison of the number of links within clusters and the expected value of this quantity in a random graph has gained a lot of attention and popularity in the last decade. Recently, Aldecoa and Marín proposed a related, but slightly different approach leading to the quality measure surprise, and reported good behavior in the context of synthetic and real world benchmarks. We show that the problem of finding a clustering with optimum surprise is N Phard. Moreover, adoi:10.1007/978-3-319-04298-5_20 fatcat:cpytwxoknzet3hhe5zmizn2ysi