JANUS: A hypothesis-driven Bayesian approach for understanding edge formation in attributed multigraphs

Lisette Espín-Noboa, Florian Lemmerich, Markus Strohmaier, Philipp Singer

Research output: Contribution to journalArticlepeer-review

Abstract (may include machine translation)

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.

Original languageEnglish
Article number16
JournalApplied Network Science
Volume2
Issue number1
DOIs
StatePublished - 1 Dec 2017
Externally publishedYes

Keywords

  • Attributed multigraphs
  • Bayesian inference
  • Edge formation
  • HypTrails
  • Multiplex

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