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

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Complex Networks & Their Applications V (COMPLEX NETWORKS 2016 2016)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 693))

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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.

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Correspondence to Lisette Espín-Noboa , Florian Lemmerich , Markus Strohmaier or Philipp Singer .

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Espín-Noboa, L., Lemmerich, F., Strohmaier, M., Singer, P. (2017). A Hypotheses-driven Bayesian Approach for Understanding Edge Formation in Attributed Multigraphs. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-50901-3_1

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