Conference paper
Detecting Hierarchical Structure in Networks
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.
Language: | English |
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Publisher: | IEEE |
Year: | 2012 |
Pages: | 1-6 |
Proceedings: | 3rd International Workshop on Cognitive Information Processing (CIP) |
ISBN: | 1467318760 , 1467318779 , 1467318787 , 9781467318761 , 9781467318778 and 9781467318785 |
ISSN: | 23271698 and 23271671 |
Types: | Conference paper |
DOI: | 10.1109/CIP.2012.6232913 |
ORCIDs: | Herlau, Tue , Mørup, Morten , Schmidt, Mikkel Nørgaard and Hansen, Lars Kai |