Accounting for missing actors in interaction network inference from abundance data
Year: 2020
Type: preprint
Abstract: Network inference aims at unraveling the dependency structure relating jointly observed variables. Graphical models provide a general framework to distinguish between marginal and conditional dependency. Unobserved variables (missing actors) may induce apparent conditional dependencies.In the context of count data, we introduce a mixture of Poisson log-normal distributions with tree-shaped graphical models, to recover the dependency structure, including missing actors. We design a variational EM algorithm and assess its performance on synthetic data. We demonstrate the ability of our approach to recover environmental drivers on two ecological datasets. The corresponding R package is available from github.com/Rmomal/nestor.
Source: arXiv (Cornell University)
Institutions: AgroParisTech, Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Mathématiques et Informatique Appliquées, Centre National de la Recherche Scientifique, Sorbonne Université, Centre d'Écologie et des Sciences de la Conservation, Laboratoire de Mathématiques et Modélisation d'Évry
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
OpenAlex ID: W4287704274