We are interested in the widespread problem of clustering documents and finding topics in large collections of written documents in the presence of metadata and hyperlinks. To tackle the challenge of accounting for these different types of datasets, we propose a novel framework based on Multilayer Networks and Stochastic Block Models. The main innovation of our approach over other techniques is that it applies the same non-parametric probabilistic framework to the different sources of datasets simultaneously. The key difference to other multilayer complex networks is the strong unbalance between the layers, with the average degree of different node types scaling differently with system size. We show that the latter observation is due to generic properties of text, such as Heaps’ law, and strongly affects the inference of communities. We present and discuss the performance of our method in different datasets (hundreds of Wikipedia documents, thousands of scientific papers, and thousands of E-mails) showing that taking into account multiple types of information provides a more nuanced view on topic- and document-clusters and increases the ability to predict missing links.

我们对聚类文档和在存在元数据和超链接的大量书面文档中查找主题的普遍问题感兴趣。为了应对解释这些不同类型数据集的挑战，我们提出了一个基于多层网络和随机块模型的新框架。我们的方法相对于其他技术的主要创新在于，它同时将相同的非参数概率框架应用于不同的数据集源。与其他多层复杂网络的主要区别在于层之间的强烈不平衡，不同节点类型的平均程度随系统规模的不同而不同。我们表明后一种观察是由于文本的通用属性，例如 Heaps 定律，并且强烈影响社区的推理。