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Bayesian Nonparametrics for Sparse Dynamic Networks

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13717))

Abstract

In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks. A positive parameter is associated to each node of a network, which models the sociability of that node. Sociabilities are assumed to evolve over time, and are modelled via a dynamic point process model. The model is able to capture long term evolution of the sociabilities. Moreover, it yields sparse graphs, where the number of edges grows subquadratically with the number of nodes. The evolution of the sociabilities is described by a tractable time-varying generalised gamma process. We provide some theoretical insights into the model and apply it to three datasets: a simulated network, a network of hyperlinks between communities on Reddit, and a network of co-occurences of words in Reuters news articles after the September \(11^{th}\) attacks.

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Notes

  1. 1.

    The name was coined by [13] after [3].

  2. 2.

    https://snap.stanford.edu/data/soc-RedditHyperlinks.html.

  3. 3.

    http://vlado.fmf.uni-lj.si/pub/networks/data/CRA/terror.htm.

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Acknowledgement

We thank the reviewers for their helpful and constructive comments. C. Naik was supported by the Engineering and Physical Sciences Research Council and Medical Research Council [award reference 1930478]. F. Caron was supported by the Engineering and Physical Sciences Council under grant EP/P026753/1. J. Rousseau was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 834175). K. Palla and Y.W. Teh were supported by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013) ERC grant agreement no. 617411.

Author information

Authors and Affiliations

  1. Department of Statistics, University of Oxford, Oxford, UK

    Cian Naik, François Caron, Judith Rousseau & Yee Whye Teh

  2. Microsoft Research, Cambridge, UK

    Konstantina Palla

  3. Google Deepmind, London, UK

    Yee Whye Teh

Authors
  1. Cian Naik
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  2. François Caron
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  3. Judith Rousseau
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  4. Yee Whye Teh
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  5. Konstantina Palla
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Corresponding author

Correspondence to Cian Naik .

Editor information

Editors and Affiliations

  1. Grenoble Alpes University, Saint Martin d’Hères, France

    Massih-Reza Amini

  2. INSA Rouen Normandy, Saint Etienne du Rouvray, France

    Stéphane Canu

  3. Ruhr-Universität Bochum, Bochum, Germany

    Asja Fischer

  4. KU Leuven, Leuven, Belgium

    Tias Guns

  5. Central European University, Vienna, Austria

    Petra Kralj Novak

  6. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Grigorios Tsoumakas

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Naik, C., Caron, F., Rousseau, J., Teh, Y.W., Palla, K. (2023). Bayesian Nonparametrics for Sparse Dynamic Networks. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13717. Springer, Cham. https://doi.org/10.1007/978-3-031-26419-1_12

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  • DOI: https://doi.org/10.1007/978-3-031-26419-1_12

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