Structure and inference in hypergraphs with node attributes.

ABSTRACT: 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 attribute

SUBMITTER: Badalyan A 

PROVIDER: S-EPMC11329712 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature