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Graph-valued regression: Prediction of unlabelled networks in a non-Euclidean graph space

Author

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  • Calissano, Anna
  • Feragen, Aasa
  • Vantini, Simone

Abstract

Understanding how unlabelled graphs depend on input values or vectors is of extreme interest in a range of applications. In this paper, we propose a regression model taking values in graph space, representing unlabelled graphs which can be weighted or unweighted, one or multi-layer, and have same or different numbers of nodes, as a function of real valued regressor. As graph space is not a manifold, well-known manifold regression models are not applicable. We provide flexible parametrized regression models for graph space, along with precise and computationally efficient estimation procedures given by the introduced align all and compute regression algorithm. We show the potential of the proposed model for three real datasets: a time dependent cryptocurrency correlation matrices, a set of bus mobility usage network in Copenhagen (DK) during the pandemic, and a set of team players’ passing networks for all the matches in Fifa World Championship 2018.

Suggested Citation

  • Calissano, Anna & Feragen, Aasa & Vantini, Simone, 2022. "Graph-valued regression: Prediction of unlabelled networks in a non-Euclidean graph space," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:jmvana:v:190:y:2022:i:c:s0047259x22000021
    DOI: 10.1016/j.jmva.2022.104950
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    References listed on IDEAS

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    1. Filipe Manuel Clemente & Fernando Manuel Lourenço Martins & Dimitris Kalamaras & P. Del Wong & Rui Sousa Mendes, 2015. "General network analysis of national soccer teams in FIFA World Cup 2014," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 15(1), pages 80-96, March.
    2. Daniele Durante & David B. Dunson, 2014. "Nonparametric Bayes dynamic modelling of relational data," Biometrika, Biometrika Trust, vol. 101(4), pages 883-898.
    3. Yang Ni & Francesco C. Stingo & Veerabhadran Baladandayuthapani, 2019. "Bayesian Graphical Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 184-197, January.
    4. Mizuno, Takayuki & Takayasu, Hideki & Takayasu, Misako, 2006. "Correlation networks among currencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 336-342.
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