% Abstract
\abstract{
    Networks are versatile objects able to represent various types of
    interactions and bipartite networks are particularly useful in ecological
    context for interaction between different entities (e.g. plant-pollinator). As the networks grow in size, reliable metrics, models and methods are needed to detect structure and perform analysis.
    Those methods exist and are pretty robust for single network analyses
    but we have motivation to consider a collection of network,
    in order to compare their structure or partition them.
    For collection of simple networks a colSBM (collection Stochastic Block Model~\cite{chabert-liddellLearningCommonStructures2024a}) has been proposed.
    We adapt this model to the bipartite case with a variational
    Expectation-Maximization algorithm for inference, a clever parameter space
    exploration and a BIC-like criterion for model selection. Building on this
    method we present a partitioning algorithm to gather networks based
    on their shared structures. We perform simulation studies to assess
    performance of our models and algorithm. Finally, we apply our clustering
    algorithm on ecological networks.
}