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：二分相互作用网络的结构可以通过为两类节点分别提供聚类来描述。此类聚类是通过将潜在区块模型（LBM）拟合于从野外物种相互作用抽样获得的观测网络而输出的。然而，抽样是有限且可能不均匀的。这可能会危及LBM的拟合，进而通过检测那些源于抽样而非实际潜在生态现象的结构，影响对网络结构的描述。如果观测到的相互作用网络是一个加权二分网络，其中两个物种之间观测到的相互作用次数是已知的，则可以估计所有物种的抽样努力度，并用于校正LBM的拟合。我们提出结合一个考虑抽样的观测模型和一个用于描述潜在可能生态相互作用结构的LBM。我们为该模型开发了一种新颖的推断方法，其有效性在模拟研究中得到了验证。我们的模型在生态学中的实际意义在一个大型植物-传粉者网络数据集上得到了凸显。