2022/09/26 by Federico Musciotto, Federico Battiston, Musciotto, Federico +3 · 1 citation
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Data Visualization and Analytics #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Topological and Geometric Data Analysis
paper · pdf · doi:10.48550/arxiv.2209.12712
openalex publication_date 2022/09/26 · openalex created_date 2022/09/28 · openalex updated_date 2026/07/28
We introduce a method for the detection of Statistically Validated Simplices in higher-order networks. Statistically validated simplices represent the maximal sets of nodes of any size that consistently interact collectively and do not include co-interacting nodes that appears only occasionally. Using properly designed higher-order benchmarks, we show that our approach is highly effective in systems where the maximal sets are likely to be diluted into interactions of larger sizes that include occasional participants. By applying our method to two real world datasets, we also show how it allows to detect simplices whose nodes are characterized by significant levels of similarity, providing new insights on the generative processes of real world higher-order networks.