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A generative model for scientific concept hierarchies.

Srayan Datta1, Eytan Adar1,2

  • 1Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, United States of America.

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Summary
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Scientific concepts evolve and branch over time, forming phylogenetic hierarchies. A new model based on preferential attachment simulates these hierarchies, aiding in understanding and predicting scientific concept evolution.

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Area of Science:

  • Bibliometrics
  • Network Science
  • History of Science

Background:

  • Scientific progress involves building upon prior research, leading to concept evolution.
  • Understanding this evolution is crucial for tracking scientific development.

Purpose of the Study:

  • To model the evolution of scientific concepts using phylogenetic hierarchies.
  • To identify key properties of these concept hierarchies and the processes driving them.

Main Methods:

  • Utilized scientific keyphrases from large academic corpora as proxies for scientific concepts.
  • Constructed phylogenetic hierarchies to represent concept relationships and temporal evolution.
  • Developed a generative model based on preferential attachment to simulate hierarchy properties.

Main Results:

  • Identified power-law distributions in degree and component size within concept hierarchies.
  • Observed the existence of a giant component and a lower probability of extending older concepts.
  • The preferential attachment model successfully replicated observed graphical and temporal properties.

Conclusions:

  • Scientific concept evolution can be effectively modeled using phylogenetic hierarchies.
  • Preferential attachment is a key mechanism driving the growth and structure of scientific knowledge.
  • The model provides insights into simulating and potentially predicting future scientific concept development.