MATria: A unified centrality algorithm

Trevor Cickovski, Vanessa Aguiar-Pulido, Giri Narasimhan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Computing centrality involves finding the most 'central' or important nodes in a network. Although potentially useful for biological networks, this can be challenging if the definition of importance is not obvious [1]. There are many different centrality algorithms with different importance definitions that return different results. This is immediately obvious in Figure 1(a), which shows the results of betweenness (red, [2]), closeness (yellow, [3]) and degree (blue, [4]) centrality on a bacterial co-occurence network [5]. Black nodes indicate mutual agreements. We color the top 20% of nodes found by each algorithm, and use appropriate color combinations for those found by two (i.e., red+yellow=orange for betweenness and closeness). As shown, due to spatial bias there is a wide variation making these results difficult to interpret or generalize. Betweenness tends to find nodes on the same path, closeness toward the middle of the network, and degree within the same strongly connected component.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538625941
DOIs
StatePublished - Nov 16 2017
Externally publishedYes
Event7th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017 - Orlando, United States
Duration: Oct 19 2017Oct 21 2017

Publication series

NameIEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS
Volume2017-October
ISSN (Print)2164-229X
ISSN (Electronic)2473-4659

Conference

Conference7th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017
Country/TerritoryUnited States
CityOrlando
Period10/19/1710/21/17

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

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