Aggregative and stochastic model of main path identification: A case study on graphene

Woondong Yeo, Seonho Kim, Jae Min Lee, Jaewoo Kang

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

This paper suggests a new method to search main path, as a knowledge trajectory, in the citation network. To enhance the performance and remedy the problems suggested by other researchers for main path analysis (Hummon and Doreian, Social Networks 11(1): 39-63, 1989), we applied two techniques, the aggregative approach and the stochastic approach. The first technique is used to offer improvement of link count methods, such as SPC, SPLC, SPNP, and NPPC, which have a potential problem of making a mistaken picture since they calculate link weights based on a individual topology of a citation link; the other technique, the second-order Markov chains, is used for path dependent search to improve the Hummon and Doreian's priority first search method. The case study on graphene that tested the performance of our new method showed promising results, assuring us that our new method can be an improved alternative of main path analysis. Our method's beneficial effects are summed up in eight aspects: (1) path dependent search, (2) basic research search rather than applied research, (3) path merge and split, (4) multiple main paths, (5) backward search for knowledge origin identification, (6) robustness for indiscriminately selected citations, (7) availability in an acyclic network, (8) completely automated search.

Original languageEnglish
Pages (from-to)633-655
Number of pages23
JournalScientometrics
Volume98
Issue number1
DOIs
Publication statusPublished - 2014 Jan

Keywords

  • Historiography
  • Main path analysis
  • Markov model
  • Quantitative method
  • Second-order Markov chains

ASJC Scopus subject areas

  • Social Sciences(all)
  • Computer Science Applications
  • Library and Information Sciences

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