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Related Experiment Videos

Centralities in simplicial complexes. Applications to protein interaction networks.

Ernesto Estrada1, Grant J Ross1

  • 1Department of Mathematics and Statistics, University of Strathclyde, 26 Richmond Street, Glasgow G11HX, UK.

Journal of Theoretical Biology
|November 13, 2017
PubMed
Summary

This study introduces simplicial centrality for analyzing complex networks, extending traditional node measures to simplicial complexes. Findings reveal how these new centralities identify essential proteins in cellular networks.

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Area of Science:

  • Network Science
  • Computational Biology
  • Mathematical Physics

Background:

  • Real-world complex systems are often modeled as complex networks.
  • Transforming these networks into simplicial complexes offers new analytical perspectives.
  • Existing node centrality measures do not fully capture the higher-order structures in simplicial complexes.

Purpose of the Study:

  • To extend node centrality concepts to simplicial centrality within simplicial complexes.
  • To mathematically analyze the properties of various simplicial centralities (degree, closeness, betweenness, eigenvector, Katz, subgraph).
  • To evaluate the efficacy of these centralities in identifying essential proteins within protein-protein interaction (PPI) networks.

Main Methods:

  • Transformation of complex networks into simplicial complexes.
Keywords:
CentralityComplex networksEssential proteinsGraph theoryNetwork theoryProtein interactionsSimplicial complexes

Related Experiment Videos

  • Mathematical formulation and analysis of simplicial centrality measures.
  • Comparative study of centrality distributions across different levels of simplicial complexes.
  • Application of simplicial centralities to detect essential proteins in biological networks.

    • Main Results:

    • Defined and characterized multiple simplicial centrality measures for simplicial complexes.
    • Investigated the degree distributions of these centralities at various levels.
    • Demonstrated distinct behaviors and differences among centrality measures across levels.
    • Showcased the varying capabilities of different simplicial centralities in identifying essential proteins.

    Conclusions:

    • Simplicial centrality provides a richer understanding of complex systems than traditional node centrality.
    • The choice of simplicial centrality measure impacts the identification of critical components like essential proteins.
    • This framework offers a novel approach for analyzing biological networks and other complex systems.