MATria: A unified centrality algorithm

Trevor Cickovski, Vanessa Aguiar-Pulido, Giri Narasimhan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Computing centrality is a foundational concept in social networking that involves finding the most "central" or important nodes. In some biological networks defining importance is difficult, which then creates challenges in finding an appropriate centrality algorithm. Results: We instead generalize the results of any k centrality algorithms through our iterative algorithm MATRIA, producing a single ranked and unified set of central nodes. Through tests on three biological networks, we demonstrate evident and balanced correlations with the results of these k algorithms. We also improve its speed through GPU parallelism. Conclusions: Our results show iteration to be a powerful technique that can eliminate spatial bias among central nodes, increasing the level of agreement between algorithms with various importance definitions. GPU parallelism improves speed and makes iteration a tractable problem for larger networks.

Original languageEnglish (US)
Article number278
JournalBMC Bioinformatics
Volume20
DOIs
StatePublished - Jun 6 2019
Externally publishedYes

Keywords

  • Centrality
  • Graphics Processing Unit (GPU)
  • Iteration
  • Networks

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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