Most real-world networks evolve over time. Existing literature proposes models for dynamic networks that are either unlabeled or assumed to have a single membership structure. On the other hand, a new family of Mixed Membership Stochastic Block Models (MMSBM) allows to model static labeled networks under the assumption of mixed-membership clustering. In this work, we propose to extend this later class of models to infer dynamic labeled networks under a mixed membership assumption. Our approach takes the form of a temporal prior on the model's parameters. It relies on the single assumption that dynamics are not abrupt. We show that our method significantly differs from existing approaches, and allows to model more complex systems --dynamic labeled networks. We demonstrate the robustness of our method with several experiments on both synthetic and real-world datasets. A key interest of our approach is that it needs very few training data to yield good results. The performance gain under challenging conditions broadens the variety of possible applications of automated learning tools --as in social sciences, which comprise many fields where small datasets are a major obstacle to the introduction of machine learning methods.

翻译：大多数真实网络会随时间演化。现有文献所提出的动态网络模型要么是无标签的，要么假定具有单一隶属结构。另一方面，一类新的混合隶属度随机块模型（MMSBM）能够在混合隶属度聚类假设下对静态有标签网络进行建模。在本工作中，我们提出扩展后一类模型，以在混合隶属度假设下推断动态有标签网络。我们的方法采用了模型参数上的时间先验，其唯一假设是动态过程非突变。我们表明该方法与现有方法存在显著差异，并且能够对更复杂的系统（动态有标签网络）进行建模。通过在合成数据集和真实世界数据集上的多项实验，我们验证了该方法的鲁棒性。该方法的一个关键优势在于：仅需极少训练数据即可获得良好结果。在挑战性条件下的性能提升拓宽了自动学习工具的潜在应用领域——例如社会科学领域，其中许多领域因小数据集而成为引入机器学习方法的主要障碍。