Detecting informative higher-order interactions in statistically validated hypergraphs

Abstract

The increasing availability of new data on biological and sociotechnical systems highlights the importance of well grounded filtering techniques to separate meaningful interactions from noise. In this work the authors propose the first method to detect informative connections of any order in statistically validated hypergraphs, showing on synthetic benchmarks and real-world systems that the highlighted hyperlinks are more informative than those extracted with traditional pairwise approaches.