Detecting Hierarchical Structure in Networks

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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Many real-world networks exhibit hierarchical organization. Previous models of hierarchies within relational data has focused on binary trees; however, for many networks it is unknown whether there is hierarchical structure, and if there is, a binary tree might not account well for it. We propose a generative Bayesian model that is able to infer whether hierarchies are present or not from a hypothesis space encompassing all types of hierarchical tree structures. For efficient inference we propose a collapsed Gibbs sampling procedure that jointly infers a partition and its hierarchical structure. On synthetic and real data we demonstrate that our model can detect hierarchical structure leading to better link-prediction than competing models. Our model can be used to detect if a network exhibits hierarchical structure, thereby leading to a better comprehension and statistical account the network.
Original languageEnglish
Title2012 3rd International Workshop on Cognitive Information Processing (CIP)
Number of pages6
PublisherIEEE
Publication date2012
ISBN (print)978-1-4673-1877-8
DOIs
StatePublished

Workshop

Workshop3rd International Workshop on Cognitive Information Processing (CIP)
CountrySpain
CityBaiona
Period28/05/1230/05/12
Internet addresshttp://cip2012.tsc.uc3m.es/
CitationsWeb of Science® Times Cited: No match on DOI
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