Work

Identifying maximal sets of significantly interacting nodes in higher-order networks

Year: 2026

Type: article

Abstract: Filtering methods are fundamental tools for extracting the backbone of complex networks. Systems displaying group interactions, however, open the way to new types of filtering approaches. Here, we introduce a statistical filtering method for higher-order networks that identifies statistically validated maximal interacting sets-maximal sets of nodes that consistently interact together within group interactions. Using properly designed 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. Applications to real-world data reveal that the identified sets of nodes are characterized by higher levels of similarity and topical coherence, highlighting the ability of our method to provide new insights on the organization of real-world higher-order networks.

Source: Physical review. E

DOI: 10.1103/t86l-31d5

Authors: Lorenzo Betti, Federico Musciotto, Federico Battiston, Rosario N. Mantegna

Institutions: Central European University, University of Palermo, Libera Università Internazionale degli Studi Sociali Guido Carli, Complexity Science Hub

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Funder: Austrian Science Fund

Awards: The structure of higher-order interaction networks, The physics of higher-order interaction networks, G9000017597