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CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis.

Minoo Ashtiani1, Mehdi Mirzaie2, Mohieddin Jafari1,3

  • 1Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.

Bioinformatics (Oxford, England)
|September 22, 2018
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Summary

Centrality analysis in networks has many measures, making it hard to choose the best one. The Central Informative Nodes in Network Analysis (CINNA) package helps compare and select the most informative centrality measure for network structures.

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Area of Science:

  • Network science
  • Computational biology
  • Data analysis

Background:

  • Centrality analysis in networks presents ambiguities due to numerous measures with differing algorithms.
  • No single centrality measure is universally preferred for identifying essential nodes based on topological features.

Purpose of the Study:

  • To develop an instrument for comparing centrality measures.
  • To identify the most informative centrality measure for a given network structure using dimensional reduction.

Main Methods:

  • Introduction of the Central Informative Nodes in Network Analysis (CINNA) package.
  • CINNA supports centrality analysis in various network types (weighted/unweighted, directed/undirected).
  • The package compares, sorts, and visualizes centrality measures to determine node importance.

Main Results:

  • CINNA provides a unified framework for comprehensive centrality analysis.
  • Facilitates the selection of the most appropriate centrality measure for network analysis.
  • Enables identification of key nodes based on network topology.

Conclusions:

  • CINNA addresses the ambiguity in centrality measure selection.
  • Offers a robust tool for network structure analysis and node essentiality identification.
  • Supports informed decision-making in network science research.