Badalyan Anna, Ruggeri Nicolò, De Bacco Caterina
Max Planck Institute for Intelligent Systems, Cyber Valley, Tübingen, Germany.
Department of Computer Science, ETH, Zürich, Switzerland.
Nat Commun. 2024 Aug 16;15(1):7073. doi: 10.1038/s41467-024-51388-5.
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.
许多通过超图编码的网络数据集，其中的单元以两个或更多的组进行交互，都伴随着关于节点的额外信息，例如个人在工作场所中的角色。在这里，我们展示了这些节点属性如何用于增进我们对高阶交互所产生结构的理解。我们考虑超图中的社区检测问题，并开发了一个有原则的模型，该模型结合了高阶交互和节点属性，以更好地表示观察到的交互，并比单独使用这两种类型的信息更准确地检测社区。该方法从输入数据中自动学习结构和属性对解释数据的贡献程度，如果属性没有信息价值，则降低其权重或予以丢弃。我们的算法实现效率高，能够扩展到大型超图和大量单元的交互。我们将我们的方法应用于各种系统，在超边预测任务以及在选择与属性相关的社区划分（当这些属性具有信息价值时）方面表现出强大的性能，否则则予以丢弃。我们的方法说明了在有高阶数据时使用有信息价值的节点属性的优势。
