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Structure and inference in hypergraphs with node attributes
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DOI：10.1038/s41467-024-51388-5.png)
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Abstract
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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. Many networked datasets encoded with hypergraphs include additional node attributes, such as roles in a workplace, that can enhance the understanding of higher-order interactions. This study presents a model that integrates higher-order interactions and node attributes for improved community detection in hypergraphs, demonstrating superior accuracy and efficiency in hyperedge prediction and community division tasks.
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