Many networked datasets with units interacting in groups of two or more, encoded with hypergraphs, are accompanied by extra information about nodes, such as the role of an individual in a workplace. Here we show how these node attributes can be used to improve our understanding of the structure resulting from higher-order interactions. We consider the problem of community detection in hypergraphs and develop a principled model that combines higher-order interactions and node attributes to better represent the observed interactions and to detect communities more accurately than using either of these types of information alone. The method learns automatically from the input data the extent to which structure and attributes contribute to explain the data, down weighing or discarding attributes if not informative. Our algorithmic implementation is efficient and scales to large hypergraphs and interactions of large numbers of units. We apply our method to a variety of systems, showing strong performance in hyperedge prediction tasks and in selecting community divisions that correlate with attributes when these are informative, but discarding them otherwise. Our approach illustrates the advantage of using informative node attributes when available with higher-order data.

翻译：许多以超图编码的网络数据集包含两个或更多单元组成的交互群组，这些数据集通常附带有节点的额外信息，例如个体在工作场所中的角色。本文展示了如何利用这些节点属性来加深对高阶交互所形成结构的理解。我们考虑超图中的社区检测问题，并构建了一个原理性模型，该模型结合了高阶交互与节点属性，能够比单独使用任一类信息更准确地表示观测到的交互并检测社区。该方法能够从输入数据中自动学习结构与属性对数据解释的贡献程度，在属性信息不具解释力时降低其权重或予以舍弃。我们的算法实现高效且可扩展至大规模超图及大量单元间的交互。我们将该方法应用于多种系统，在超边预测任务中表现出优异性能，并能在属性信息有效时选择与之相关的社区划分，否则将其舍弃。本方法阐明了在可获得高阶数据时，利用具有信息量的节点属性所带来的优势。