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A Q-analysis package for higher-order interactions analysis in Python and its application in network physiology.

Nikita Smirnov1, Semen Kurkin2, Alexander E Hramov2

  • 1Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia.

Frontiers in Network Physiology
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PubMed
Summary
This summary is machine-generated.

This study introduces a Python package for Q-analysis, enabling the study of complex network structures. The tool reveals higher-order topological signatures and network disruptions, aiding research in social networks and neuroscience.

Keywords:
Q-analysiscomplex networksfunctional networkshigher-order interactionsnetwork physiologynetwork topologysimplex

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Area of Science:

  • Network Science
  • Computational Mathematics
  • Data Analysis

Background:

  • Real-world networks exhibit complex, higher-order structures missed by traditional pairwise analysis.
  • Q-analysis, using simplicial complexes, offers a framework for multi-node interaction quantification.
  • Limited accessible software has hindered Q-analysis adoption in network science.

Purpose of the Study:

  • Introduce a comprehensive Python package for Q-analysis methodology.
  • Enable construction of simplicial complexes and computation of key metrics (structure vectors, topological entropy).
  • Facilitate exploration of higher-order interactions in complex systems.

Main Methods:

  • Developed a Python package implementing Q-analysis core methodology.
  • Included routines for simplicial complex construction from graphs/simplex lists.
  • Integrated machine learning (scikit-learn) and statistical inference (permutation tests).

Main Results:

  • Simulation study revealed distinct higher-order topological signatures in scale-free vs. configurational networks.
  • Analysis of DBLP co-authorship data showed evolving collaboration structures over three decades.
  • Identified disruptions in higher-order organization of brain networks in Major Depressive Disorder (MDD).

Conclusions:

  • The Python package democratizes Q-analysis for broader research accessibility.
  • Applications demonstrate utility across social networks, collaboration networks, and neuroscience.
  • This open-source tool enables quantitative analysis of intricate, multi-node network structures.