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Understanding the evolution of science: analyzing evolving term co-occurrence graphs with spectral techniques

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Abstract

Given the high number of scientific papers that are published every year, it is a challenge to observe the evolution of scientific fields. While, for example, computer science and biology were considered rather unrelated 40 years ago, today, bioinformatics is a well-established field. One way to analyze these questions is to observe the evolution of the term co-occurrence graphs of the abstracts of scientific publications. In a term co-occurrence graph, two terms are connected if they appear together in an abstract of a publication. We weight the edges of this graph with the number of common occurrences. We analyze the evolution of this co-occurrence graph, that we constructed for each year, on the basis of a large collection of scientific articles, with the help of spectral techniques. We present our preliminary observations and discuss our ongoing work.
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Dates and versions

hal-02195026 , version 1 (26-07-2019)

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  • HAL Id : hal-02195026 , version 1

Cite

Zoltan Miklos, Mickaël Foursov, Franklin Lia, Ian Jeantet, David Gross-Amblard. Understanding the evolution of science: analyzing evolving term co-occurrence graphs with spectral techniques. Third international workshop on advances on managing and mining evolving graphs (LEG@ECMLPKDD), Sep 2019, Würzburg, Germany. ⟨hal-02195026⟩
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