Graph Clustering with Surprise: Complexity and Exact Solutions [chapter]

Tobias Fleck, Andrea Kappes, Dorothea Wagner
2014 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, a
more » ... bicriterial view on the problem permits to compute optimum solutions for small instances by solving a small number of integer linear programs, and leads to a polynomial time algorithm on trees.
doi:10.1007/978-3-319-04298-5_20 fatcat:cpytwxoknzet3hhe5zmizn2ysi