Generalized multipartite networks consist in the joint observation of several networks implying some common pre-specified groups of individuals. Such complex networks arise commonly in social sciences, biology, ecology, etc. We propose a flexible probabilistic model named Multipartite Block Model (MBM) able to unravel the topology of multipartite networks by identifying clusters (blocks) of nodes sharing the same patterns of connectivity across the collection of networks they are involved in. The model parameters are estimated through a variational version of the Expectation–Maximization algorithm. The numbers of blocks are chosen using an Integrated Completed Likelihood criterion specifically designed for our model. A simulation study illustrates the robustness of the inference strategy. Finally, two datasets respectively issued from ecology and ethnobiology are analyzed with the MBM in order to illustrate its flexibility and its relevance for the analysis of real datasets.

The inference procedure is implemented in an R-package GREMLIN, available on Github (https://github.com/Demiperimetre/GREMLINhttps://github.com/Demiperimetre/GREMLIN).

广义多方网络包括对几个网络的联合观察，这暗示着一些常见的预先指定的个人群体。这种复杂的网络通常出现在社会科学，生物学，生态学等领域。我们提出了一种称为多部分块模型（MBM）的灵活概率模型，该模型能够通过识别共享相同连通性模式的节点的簇（块）来揭示多部分网络的拓扑结构跨模型所涉及的网络集合。通过“期望最大化”算法的变体版本来估计模型参数。使用专为我们的模型设计的“综合完成可能性”准则选择块的数量。仿真研究说明了推理策略的鲁棒性。最后，

推理过程在 [R-包 格林林，可在Github（https://github.com/Demiperimetre/GREMLINhttps://github.com/Demiperimetre/GREMLIN）上找到。