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Peer-reviewedCC BYPhysical review. E

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

  • Anonymous1
  • Federico Musciotto2
  • Federico Battiston1,3
  • Rosario N. Mantegna4,2
4 affiliations
  1. 1Central European University
  2. 2Università di Palermo
  3. 3Luiss University of Rome
  4. 4Complexity Science Hub
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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.

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Cite

BibTeXRIS
BibTeX
@article{anonymous2026identifying,
  title = {{Identifying maximal sets of significantly interacting nodes in higher-order networks}},
  author = {Anonymous and Federico Musciotto and Federico Battiston and Rosario N. Mantegna},
  journal = {Physical review. E},
  year = {2026},
  month = jun,
  doi = {10.1103/t86l-31d5},
  url = {https://doi.org/10.1103/t86l-31d5},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Identifying maximal sets of significantly interacting nodes in higher-order networks
AU  - Anonymous
AU  - Federico Musciotto
AU  - Federico Battiston
AU  - Rosario N. Mantegna
PY  - 2026
DA  - 2026/06/30
JO  - Physical review. E
DO  - 10.1103/t86l-31d5
UR  - https://doi.org/10.1103/t86l-31d5
AB  - 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.
SN  - 2470-0045
SN  - 2470-0053
SN  - 2470-0061
ER  -