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Accounting for missing actors in interaction network inference from abundance data

Raphaëlle Momal, Stéphane Robin and Christophe Ambroise

Journal of the Royal Statistical Society Series C, 2021, vol. 70, issue 5, 1230-1258

Abstract: Network inference aims at unravelling 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 data sets. The corresponding R package is available from

Date: 2021
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Handle: RePEc:bla:jorssc:v:70:y:2021:i:5:p:1230-1258