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Evaluating the Natural Variability in Generative Models for Complex Networks

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Complex Networks and Their Applications VII (COMPLEX NETWORKS 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 812))

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Abstract

Complex networks are used to represent real-world systems using sets of nodes and edges that represent elements and their interactions, respectively. A principled approach to understand these network structures (and the processes that give rise to them) is to formulate generative models and infer their parameters from given data. Ideally, a generative model should be able to synthesize networks that belong to the same population as the observed data, but most models are not designed to accomplish this task. Due to the scarcity of data in the form of populations of networks, generative models are typically formulated to learn parameters from a single network observation, hence ignoring the natural variability of network populations. In this paper, we evaluate four generative models with respect to their ability to synthesize networks that belong to the same population as the observed network. Our empirical analysis quantifying the ability of network models to replicate characteristics of a population of networks highlights the need for rethinking the way we evaluate the goodness of fit of new and existing network models.

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Notes

  1. 1.

    Infomap community detection algorithm [26] was used in our experiments.

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Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. 1762633.

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Authors and Affiliations

  1. School of Industrial Engineering, Purdue University, West Lafayette IN, 47907, USA

    Viplove Arora & Mario Ventresca

Authors
  1. Viplove Arora
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  2. Mario Ventresca
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Corresponding author

Correspondence to Mario Ventresca .

Editor information

Editors and Affiliations

  1. Nokia Bell Labs, Cambridge, UK

    Luca Maria Aiello

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I UMR CNRS 6306 9, University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  4. Mathematical Institute, University of Oxford, Oxford, UK

    Renaud Lambiotte

  5. Department of Computer Science and Technology, The Computer Laboratory, University of Cambridge, Cambridge, UK

    Pietro Lió

  6. Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

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Arora, V., Ventresca, M. (2019). Evaluating the Natural Variability in Generative Models for Complex Networks. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_59

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