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Accounting for missing actors in interaction network inference from abundance data

by   Raphaëlle Momal, et al.

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


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Code Repositories


Meet nestor, an R package for the variational inference of species interaction networks from abundance data, while accounting for missing actors.

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