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Time evolution of the hierarchical networks between PubMed MeSH terms

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  • Sámuel G Balogh
  • Dániel Zagyva
  • Péter Pollner
  • Gergely Palla

Abstract

Hierarchical organisation is a prevalent feature of many complex networks appearing in nature and society. A relating interesting, yet less studied question is how does a hierarchical network evolve over time? Here we take a data driven approach and examine the time evolution of the network between the Medical Subject Headings (MeSH) provided by the National Center for Biotechnology Information (NCBI, part of the U. S. National Library of Medicine). The network between the MeSH terms is organised into 16 different, yearly updated hierarchies such as “Anatomy”, “Diseases”, “Chemicals and Drugs”, etc. The natural representation of these hierarchies is given by directed acyclic graphs, composed of links pointing from nodes higher in the hierarchy towards nodes in lower levels. Due to the yearly updates, the structure of these networks is subject to constant evolution: new MeSH terms can appear, terms becoming obsolete can be deleted or be merged with other terms, and also already existing parts of the network may be rewired. We examine various statistical properties of the time evolution, with a special focus on the attachment and detachment mechanisms of the links, and find a few general features that are characteristic for all MeSH hierarchies. According to the results, the hierarchies investigated display an interesting interplay between non-uniform preference with respect to multiple different topological and hierarchical properties.

Suggested Citation

  • Sámuel G Balogh & Dániel Zagyva & Péter Pollner & Gergely Palla, 2019. "Time evolution of the hierarchical networks between PubMed MeSH terms," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-15, August.
    • Handle: RePEc:plo:pone00:0220648
    DOI: 10.1371/journal.pone.0220648
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    References listed on IDEAS

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    1. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
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    Cited by:

    1. Lu, Kun & Yang, Guancan & Wang, Xue, 2022. "Topics emerged in the biomedical field and their characteristics," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    2. Ekaterina V. Ilgisonis & Mikhail A. Pyatnitskiy & Svetlana N. Tarbeeva & Artem A. Aldushin & Elena A. Ponomarenko, 2022. "How to catch trends using MeSH terms analysis?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1953-1967, April.

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