Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
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.
|Title||2012 3rd International Workshop on Cognitive Information Processing (CIP)|
|Number of pages||6|
|Workshop||3rd International Workshop on Cognitive Information Processing (CIP)|
|Period||28/05/12 → 30/05/12|
|Citations||Web of Science® Times Cited: No match on DOI|
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