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dc.contributor.author
Casiraghi, Giona
dc.contributor.author
Nanumyan, Vahan
dc.contributor.author
Scholtes, Ingo
dc.contributor.author
Schweitzer, Frank
dc.date.accessioned
2019-08-13T13:19:57Z
dc.date.available
2017-06-12T17:22:37Z
dc.date.available
2019-08-13T13:19:57Z
dc.date.issued
2016
dc.identifier.uri
http://hdl.handle.net/20.500.11850/124256
dc.description.abstract
Statistical ensembles define probability spaces of all networks consistent with given aggregate statistics and have become instrumental in the analysis of relational data on networked systems. Their numerical and analytical study provides the foundation for the inference of topological patterns, the definition of network-analytic measures, as well as for model selection and statistical hypothesis testing. Contributing to the foundation of these important data science techniques, in this article we introduce generalized hypergeometric ensembles, a framework of analytically tractable statistical ensembles of finite, directed and weighted networks. This framework can be interpreted as a generalization of the classical configuration model, which is commonly used to randomly generate networks with a given degree sequence or distribution. Our generalization rests on the introduction of dyadic link propensities, which capture the degree-corrected tendencies of pairs of nodes to form edges between each other. Studying empirical and synthetic data, we show that our approach provides broad perspectives for community detection, model selection and statistical hypothesis testing.
en_US
dc.language.iso
en
en_US
dc.publisher
Cornell University
en_US
dc.title
Generalized Hypergeometric Ensembles: Statistical Hypothesis Testing in Complex Networks
en_US
dc.type
Working Paper
ethz.journal.title
arXiv
ethz.pages.start
1607.02441
en_US
ethz.size
5 p.
en_US
ethz.identifier.arxiv
1607.02441
ethz.publication.place
Ithaca, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03682 - Schweitzer, Frank / Schweitzer, Frank
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03682 - Schweitzer, Frank / Schweitzer, Frank
ethz.relation.isPartOf
10.3929/ethz-b-000282134
ethz.date.deposited
2017-06-12T17:23:24Z
ethz.source
ECIT
ethz.identifier.importid
imp593654fa34fcc67366
ethz.ecitpid
pub:186716
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2017-07-20T13:54:20Z
ethz.rosetta.lastUpdated
2019-08-13T13:20:13Z
ethz.rosetta.exportRequired
false
ethz.rosetta.versionExported
true
ethz.COinS
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