Time evolution of the hierarchical networks between PubMed MeSH terms

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.

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“…The overview in Figure 1 highlights the most active areas of the literature. To understand what each cluster involves, one option is to characterize the thematic structure of the cluster in a hierarchical representation of its component concepts (Palla Balogh et al, 2019). Given a set of text documents such as titles, abstracts, and citation contexts, constructing a concept tree in CiteSpace consists of the following steps:…”

“…The overview in Figure 1 highlights the most active areas of the literature. To understand what each cluster involves, one option is to characterize the thematic structure of the cluster in a hierarchical representation of its component concepts ( Palla et al, 2015 ; Balogh et al, 2019 ). Given a set of text documents such as titles, abstracts, and citation contexts, constructing a concept tree in CiteSpace consists of the following steps: Extracting noun phrases from the text preprocessed with part-of-speech tagging; Deriving hierarchical relations between phrases at the sentence level such that if a noun phrase n A co-occurs with noun phrases n B and n C , then n A is considered as a higher-level concept on the hierarchical structure; Visualizing the hierarchical structure as a concept tree.…”

“…To understand what each cluster involves, one option is to characterize the thematic structure of the cluster in a hierarchical representation of its component concepts (Palla, Tibely et al 2015, Balogh, Zagyva et al 2019. Figure 2 shows a concept tree of Cluster #5 pregnant women.…”

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“…Furthermore, the x -index [ 9 ] used in this study generalizes the h -index [ 7 ], which is determined by the square area (i.e., the number of publications equal to the citation point). The x -index thus surpasses the h-index in differentiating the personal IRA among affiliated countries/areas, authors, or the medical subject headings (MeSH terms) [ 28 ].…”

How to catch trends using MeSH terms analysis?

“…Balogh et al examined various statistical properties of the Mesh term networks 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 (Balogh et al, 2019 ).…”