In this work, we propose an original method for aggregating multiple clustering coming from different sources of information. Each partition is encoded by a co-membership matrix between observations. Our approach uses a mixture of multilayer Stochastic Block Models (SBM) to group co-membership matrices with similar information into components and to partition observations into different clusters, taking into account their specificities within the components. The identifiability of the model parameters is established and a variational Bayesian EM algorithm is proposed for the estimation of these parameters. The Bayesian framework allows for selecting an optimal number of clusters and components. The proposed approach is compared using synthetic data with consensus clustering and tensor-based algorithms for community detection in large-scale complex networks. Finally, the method is utilized to analyze global food trading networks, leading to structures of interest.

翻译：本文提出了一种新颖的聚合来自不同信息源的多重聚类结果的方法。每个划分通过观测值之间的共隶属矩阵进行编码。我们的方法采用多层随机块模型混合来将信息相似的共隶属矩阵归入组件，并根据观测值在组件内的特性将其划分为不同簇。模型参数的辨识性得到证明，并提出了变分贝叶斯期望最大化算法用于参数估计。贝叶斯框架允许选择最优的簇数和组件数。通过合成数据与共识聚类及基于张量的社区检测算法在大规模复杂网络中的比较，验证了所提方法的有效性。最后，该方法被用于分析全球食品贸易网络，得到了具有研究价值的结构。