Networked datasets are often enriched by different types of information about individual nodes or edges. However, most existing methods for analyzing such datasets struggle to handle the complexity of heterogeneous data, often requiring substantial model-specific analysis. In this paper, we develop a probabilistic generative model to perform inference in multilayer networks with arbitrary types of information. Our approach employs a Bayesian framework combined with the Laplace matching technique to ease interpretation of inferred parameters. Furthermore, the algorithmic implementation relies on automatic differentiation, avoiding the need for explicit derivations. This makes our model scalable and flexible to adapt to any combination of input data. We demonstrate the effectiveness of our method in detecting overlapping community structures and performing various prediction tasks on heterogeneous multilayer data, where nodes and edges have different types of attributes. Additionally, we showcase its ability to unveil a variety of patterns in a social support network among villagers in rural India by effectively utilizing all input information in a meaningful way.

翻译：网络数据集通常通过关于个体节点或边的不同类型信息得以丰富。然而，现有的大多数分析方法难以处理异构数据的复杂性，往往需要进行大量针对特定模型的分析。本文提出一种概率生成模型，用于在包含任意类型信息的多层网络中进行推断。我们的方法采用贝叶斯框架并结合拉普拉斯匹配技术，以简化对推断参数的解释。此外，算法实现依赖于自动微分，避免了显式推导的需求。这使得我们的模型具有可扩展性，并能灵活适应任何输入数据的组合。我们证明了该方法在检测重叠社区结构以及在异构多层数据（其中节点和边具有不同类型的属性）上执行多种预测任务的有效性。此外，通过有效利用所有输入信息，我们展示了该方法在揭示印度农村村民社会支持网络中多种模式的能力。