Block models for generalized multipartite networks: Applications in ecology and ethnobiology

Bar-Hen, Avner; Barbillon, Pierre; Donnet, Sophie

doi:10.1177/1471082x20963254

Cited by 15 publications

(10 citation statements)

References 43 publications

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“…The idea behind these models is very general and could be extended to other types of networks. In ecology, bipartite and multipartite networks are common and the model extension (Govaert and Nadif, 2003;Bar-Hen et al, 2020) is straightforward (although some additional modeling choices arise when considering π-colSBM , δ-colSBM or δπ-colSBM ), the main difficulty would then lie in the algorithmic part. Additionally, incorporating the type of ecological interaction as a network covariate (Mariadassou et al, 2010) would help us understand its impact on the structure of the networks and to the robustness of the ecosystems they depict (Chabert-Liddell et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Learning common structures in a collection of networks. An application to food webs

Chabert-Liddell¹,

Barbillon²,

Donnet³

2022

Preprint

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Let a collection of networks represent interactions within several (social or ecological) systems. Two main issues arise: identifying similarities between the topological structures of the networks or clustering the networks according to the similarities in their structures. We tackle these two questions with a probabilistic model based approach. We propose an extension of the Stochastic Block Model (SBM) adapted to the joint modeling of a collection of networks. The networks in the collection are assumed to be independent realizations of SBMs. The common connectivity structure is imposed through the equality of some parameters.The model parameters are estimated with a variational Expectation-Maximization (EM) algorithm. We derive an ad-hoc penalized likelihood criterion to select the number of blocks and to assess the adequacy of the consensus found between the structures of the different networks. This same criterion can also be used to cluster networks on the basis of their connectivity structure. It thus provides a partition of the collection into subsets of structurally homogeneous networks.The relevance of our proposition is assessed on two collections of ecological networks. First, an application to three stream food webs reveals the homogeneity of their structures and the correspondence between groups of species in different ecosystems playing equivalent ecological roles. Moreover, the joint analysis allows a finer analysis of the structure of smaller networks. Second, we cluster 67 food webs according to their connectivity structures and demonstrate that five mesoscale structures are sufficient to describe this collection.

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Section: Discussionmentioning

confidence: 99%

Learning common structures in a collection of networks. An application to food webs

Chabert-Liddell¹,

Barbillon²,

Donnet³

2022

Preprint

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“…This variational EM algorithm is theoretically grounded (Bickel et al, 2013) and has proven its pratical efficiency (Daudin et al, 2008;Mariadassou et al, 2010). In practice for the inference of these models, we use the blockmodels R package (Leger, 2015) and to handle missing observations the GREMLINS R package (Bar-Hen et al, 2020), both available on CRAN .…”

Section: Analysis Of a Collection Of Observed Bipartite Ecological Ne...mentioning

confidence: 99%

“…Since Allesina and Pascual (2009) has advocated for the use of groups in ecological networks, SBMs (sometimes referred to as group models) have gained in popularity. Some variants adapted to multilayer ecological networks have been proposed: for multiplex networks (Kéfi et al, 2016), multipartite networks (Bar-Hen et al, 2020) or temporal networks (Matias et al, 2017). Besides, they have been used to answer specific ecological questions.…”

Section: Introductionmentioning

confidence: 99%

Impact of the mesoscale structure of a bipartite ecological interaction network on its robustness through a probabilistic modeling

Chabert-Liddell,

Barbillon,

Donnet

2021

Preprint

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The robustness of an ecological network quantifies the resilience of the ecosystem it represents to species loss. It corresponds to the proportion of species that are disconnected from the rest of the network when extinctions occur sequentially. Classically, the robustness is calculated for a given network, from the simulation of a large number of extinction sequences. The link between network structure and robustness remains an open question.Setting a joint probabilistic model on the network and the extinction sequences allows to analyze this relation. Bipartite stochastic block models have proven their ability to model bipartite networks e.g. plant-pollinator networks: species are divided into blocks and interaction probabilities are determined by the blocks of membership. Analytical expressions of the expectation and variance of robustness are obtained under this model, for different distributions of primary extinction sequences. The impact of the network structure on the robustness is analyzed through a set of properties and numerical illustrations. The analysis of a collection of bipartite ecological networks allows us to compare the empirical approach to our probabilistic approach, and illustrates the relevance of the

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“…Since Allesina and Pascual (2009) has advocated for the use of groups in ecological networks, SBMs (sometimes referred to as group models) have gained in popularity. Some variants adapted to multilayer ecological networks have been proposed for: multiplex networks (Kéfi et al, 2016), multipartite networks (Bar-Hen et al, 2020) or temporal networks (Matias & Miele, 2017). Besides, they have been used to answer specific ecological questions.…”

Section: Introductionmentioning

confidence: 99%

Impact of the mesoscale structure of a bipartite ecological interaction network on its robustness through a probabilistic modeling

2021

Self Cite

View full text Add to dashboard Cite

The robustness of an ecological network quantifies the resilience of the ecosystem it represents to species loss. It corresponds to the proportion of species that are disconnected from the rest of the network when extinctions occur sequentially. Classically, the robustness is calculated for a given network, from the simulation of a large number of extinction sequences. The link between network structure and robustness remains an open question. Setting a joint probabilistic model on the network and the extinction sequences allows analysis of this relation. Bipartite stochastic block models have proven their ability to model bipartite networks for example, plant-pollinator networks: species are divided into blocks and interaction probabilities are determined by the blocks of membership. Analytical expressions of the expectation and variance of robustness are obtained under this model, for different distributions of primary extinction sequences. The impact of the network structure on the robustness is analyzed through a set of properties and numerical illustrations. The analysis of a collection of bipartite ecological networks allows us to compare the empirical approach to our probabilistic approach, and illustrates the relevance of the latter when it comes to computing the robustness of a partially observed or incompletely sampled network.

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Block models for generalized multipartite networks: Applications in ecology and ethnobiology

Cited by 15 publications

References 43 publications

Learning common structures in a collection of networks. An application to food webs

Learning common structures in a collection of networks. An application to food webs

Impact of the mesoscale structure of a bipartite ecological interaction network on its robustness through a probabilistic modeling

Impact of the mesoscale structure of a bipartite ecological interaction network on its robustness through a probabilistic modeling

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