The structure of a bipartite interaction network can be described by providing a clustering for each of the two types of nodes. Such clusterings are outputted by fitting a Latent Block Model (LBM) on an observed network that comes from a sampling of species interactions in the field. However, the sampling is limited and possibly uneven. This may jeopardize the fit of the LBM and then the description of the structure of the network by detecting structures which result from the sampling and not from actual underlying ecological phenomena. If the observed interaction network consists of a weighted bipartite network where the number of observed interactions between two species is available, the sampling efforts for all species can be estimated and used to correct the LBM fit. We propose to combine an observation model that accounts for sampling and an LBM for describing the structure of underlying possible ecological interactions. We develop an original inference procedure for this model, the efficiency of which is demonstrated on simulation studies. The pratical interest in ecology of our model is highlighted on a large dataset of plant-pollinator network.

翻译：二分互作网络的结构可通过为两类节点分别提供聚类来描述。此类聚类结果源于对野外物种互作采样获得的观测网络进行潜在分块模型（LBM, Latent Block Model）拟合。然而，采样过程存在有限性与非均匀性，这可能导致LBM拟合失准，进而使检测到的网络结构反映采样过程而非实际生态现象的本质特征。当观测到的互作网络为加权二分网络（记录两物种间观测到的互作次数）时，可估算各物种种群采样强度，并将其用于校正LBM拟合。我们提出将描述采样过程的观测模型与刻画潜在生态互作结构的LBM相结合。为此开发了一套原创性推断流程，并通过模拟研究验证了其有效性。基于植物-传粉者网络大型数据集的分析，凸显了本模型在生态学中的实践价值。