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.
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Notes
Sink nodes are defined as those nodes that are not cited by other nodes but only cite other nodes in the network.
NPPC of 10 is: 3–12, 3–15, 3–20, 3–21, 3–22, 5–12, 5–15, 5–20, 5–21, and 5–22.
SPLC of 6 is: 3–22 via 15, 3–22 via 21, 3–22 via 20 without 21, 5–22 via 15, 5–22 via 21, and 5–22 via 20 without 21.
SPNP of 14 is: 3–12, 3–15, 3–22 via 15, 3–20, 3–21 via 20, 3–22 via 21, 3–22 via 20 without 21, 5–12, 5–15, 5–22 via 15, 5–20, 5–21 via 20, 5–22 via 21, and 5–22 via 20 without 21.
Source nodes are defined as those nodes that are cited by other nodes but do not cite any nodes in the network.
For an example of SPC, the link from node 5 to node 12 in Fig. 2 has the SPC value of 3, and the paths is: 3–22 via 15, 3–22 via 21, 3–22 via 20 without 21.
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Acknowledgments
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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Yeo, W., Kim, S., Lee, JM. et al. Aggregative and stochastic model of main path identification: a case study on graphene. Scientometrics 98, 633–655 (2014). https://doi.org/10.1007/s11192-013-1140-3
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DOI: https://doi.org/10.1007/s11192-013-1140-3