Detecting Hierarchical Structure in Networks

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    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
    Title of host publication2012 3rd International Workshop on Cognitive Information Processing (CIP)
    Number of pages6
    Publication date2012
    ISBN (Print)978-1-4673-1877-8
    Publication statusPublished - 2012
    Event3rd International Workshop on Cognitive Information Processing (CIP) - Baiona, Spain
    Duration: 28 May 201230 May 2012


    Workshop3rd International Workshop on Cognitive Information Processing (CIP)
    Internet address


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