, Volume 98, Issue 1, pp 633–655 | Cite as

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

  • Woondong Yeo
  • Seonho Kim
  • Jae-Min Lee
  • Jaewoo Kang


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.


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

Mathematics Subject Classification


JEL Classification




We, the authors of this paper, wish to record our thanks to Professor Sungyoul Choi, the director of the graphene research center at Korea Advanced Institute of Science and Technology (KAIST), who was willing to give us technical assistance related to the historical characteristics of graphene research and to the comparative analysis of the results of experiments. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (2012R1A2A2A01014729) and the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2012M3C4A7033341).


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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2013

Authors and Affiliations

  • Woondong Yeo
    • 1
    • 2
  • Seonho Kim
    • 2
  • Jae-Min Lee
    • 2
  • Jaewoo Kang
    • 1
  1. 1.Department of Computer Science & EngineeringKorea UniversitySeoulKorea
  2. 2.Technology Opportunity Research TeamKorea Institute of Science and Technology InformationSeoulKorea

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