JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs
Abstract Understanding edge formation represents a key question in network analysis. Various approaches have been postulated across disciplines ranging from network growth models to statistical (regression) methods. In this work, we extend this existing arsenal of methods with JANUS, a hypothesis-dr...
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Online Access: | http://link.springer.com/article/10.1007/s41109-017-0036-1 |
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doaj-53d71d8a031148e78eacd8e8ac14e7f92020-11-24T21:19:08ZengSpringerOpenApplied Network Science2364-82282017-06-012112010.1007/s41109-017-0036-1JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphsLisette Espín-Noboa0Florian Lemmerich1Markus Strohmaier2Philipp Singer3GESIS - Leibniz Institute for the Social SciencesGESIS - Leibniz Institute for the Social SciencesGESIS - Leibniz Institute for the Social SciencesGESIS - Leibniz Institute for the Social SciencesAbstract Understanding edge formation represents a key question in network analysis. Various approaches have been postulated across disciplines ranging from network growth models to statistical (regression) methods. In this work, we extend this existing arsenal of methods with JANUS, a hypothesis-driven Bayesian approach that allows to intuitively compare hypotheses about edge formation in multigraphs. We model the multiplicity of edges using a simple categorical model and propose to express hypotheses as priors encoding our belief about parameters. Using Bayesian model comparison techniques, we compare the relative plausibility of hypotheses which might be motivated by previous theories about edge formation based on popularity or similarity. We demonstrate the utility of our approach on synthetic and empirical data. JANUS is relevant for researchers interested in studying mechanisms explaining edge formation in networks from both empirical and methodological perspectives.http://link.springer.com/article/10.1007/s41109-017-0036-1Edge formationBayesian inferenceAttributed multigraphsMultiplexHypTrails |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lisette Espín-Noboa Florian Lemmerich Markus Strohmaier Philipp Singer |
spellingShingle |
Lisette Espín-Noboa Florian Lemmerich Markus Strohmaier Philipp Singer JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs Applied Network Science Edge formation Bayesian inference Attributed multigraphs Multiplex HypTrails |
author_facet |
Lisette Espín-Noboa Florian Lemmerich Markus Strohmaier Philipp Singer |
author_sort |
Lisette Espín-Noboa |
title |
JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs |
title_short |
JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs |
title_full |
JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs |
title_fullStr |
JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs |
title_full_unstemmed |
JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs |
title_sort |
janus: a hypothesis-driven bayesian approach for understanding edge formation in attributed multigraphs |
publisher |
SpringerOpen |
series |
Applied Network Science |
issn |
2364-8228 |
publishDate |
2017-06-01 |
description |
Abstract Understanding edge formation represents a key question in network analysis. Various approaches have been postulated across disciplines ranging from network growth models to statistical (regression) methods. In this work, we extend this existing arsenal of methods with JANUS, a hypothesis-driven Bayesian approach that allows to intuitively compare hypotheses about edge formation in multigraphs. We model the multiplicity of edges using a simple categorical model and propose to express hypotheses as priors encoding our belief about parameters. Using Bayesian model comparison techniques, we compare the relative plausibility of hypotheses which might be motivated by previous theories about edge formation based on popularity or similarity. We demonstrate the utility of our approach on synthetic and empirical data. JANUS is relevant for researchers interested in studying mechanisms explaining edge formation in networks from both empirical and methodological perspectives. |
topic |
Edge formation Bayesian inference Attributed multigraphs Multiplex HypTrails |
url |
http://link.springer.com/article/10.1007/s41109-017-0036-1 |
work_keys_str_mv |
AT lisetteespinnoboa janusahypothesisdrivenbayesianapproachforunderstandingedgeformationinattributedmultigraphs AT florianlemmerich janusahypothesisdrivenbayesianapproachforunderstandingedgeformationinattributedmultigraphs AT markusstrohmaier janusahypothesisdrivenbayesianapproachforunderstandingedgeformationinattributedmultigraphs AT philippsinger janusahypothesisdrivenbayesianapproachforunderstandingedgeformationinattributedmultigraphs |
_version_ |
1726006795020795904 |