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

DOI10.1016/j.jmva.2022.104950OpenAlexW4210694049WikidataQ114157886 ScholiaQ114157886MaRDI QIDQ2140851

Aasa Feragen, Anna Calissano, Simone Vantini

Publication date: 23 May 2022

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2022.104950

zbMATH Keywords

public transportfootball players passing networkgraph-valued datagraph-valued regressionintrinsic geometric statisticsnetwork-data

Mathematics Subject Classification ID

Linear inference, regression (62J99) Metric geometry (51F99) Graph theory (05C99) Multivariate analysis (62Hxx)

Related Items (5)

Statistical shape analysis of brain arterial networks (BAN) ⋮ Bures–Wasserstein Minimizing Geodesics between Covariance Matrices of Different Ranks ⋮ Towards quotient barycentric subspaces ⋮ Distances and isomorphism between networks: stability and convergence of network invariants ⋮ On the theory of dynamic graph regression problem

Uses Software

• GitHub
• GraphSpace
• gBoost

Cites Work

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• Fréchet regression for random objects with Euclidean predictors
• A network analysis of the 2010 FIFA world cup champion team play
• Multilinear tensor regression for longitudinal relational data
• gBoost: a mathematical programming approach to graph classification and regression
• Geodesic regression and the theory of least squares on Riemannian manifolds
• Object oriented data analysis: sets of trees
• Intrinsic polynomials for regression on Riemannian manifolds
• Overview of object oriented data analysis
• Nonparametric Bayes dynamic modelling of relational data
• Bayesian Graphical Regression

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