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A Hypotheses-driven Bayesian Approach for Understanding Edge Formation in Attributed Multigraphs

  • Lisette Espín-NoboaEmail author
  • Florian LemmerichEmail author
  • Markus StrohmaierEmail author
  • Philipp SingerEmail author
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 693)

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 a hypotheses-driven Bayesian approach that allows to intuitively compare hypotheses about edge formation on attributed 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. This work is relevant for researchers interested in studying mechanisms explaining edge formation in networks.

Keywords

Adjacency Matrix Marginal Likelihood Dirichlet Distribution Node Attribute Edge Formation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.GESISUniversity of Koblenz-LandauMainzGermany

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