How Robust Are Different Versions of Graphical Model Selection Algorithms

Kostylev Ilya () and Kalyagin Valeriy ()
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Kostylev Ilya: HSE University, Laboratory of Algorithms and Technologies for Network Analysis
Kalyagin Valeriy: HSE University, Laboratory of Algorithms and Technologies for Network Analysis

SN Operations Research Forum, 2025, vol. 6, issue 4, 1-15

Abstract: Abstract Graphical modelling has become a useful tool in modern data mining. Graphical model selection by observations is an important challenge for various practical applications of this technique. Optimization-based approaches are a basis for many fast and efficient algorithms to solve this problem. Many known properties of graphical model selection algorithms are related to the case of the Gaussian distribution of the data. The case of non-Gaussian distributions is less investigated. An important issue is to understand how sensitive developed algorithms are to a change of distribution. In the present paper, we propose a simple methodology for comparative analysis of the sensitivity of graphical model selection algorithms to the change of distribution. This methodology is applied via extensive numerical experiments for the estimation of the dependency of the quality of chosen model selection algorithms on the change of distribution. Some interesting phenomena are observed and discussed.

Keywords: Probabilistic graphical model; Graphical model selection algorithms; Uncertainty of graphical model selection algorithms; Robustness of graphical model selection algorithms; Graphical lasso algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00588-w

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