Abstract
The last few years have seen a growing interest in main path analysis among scholars across a wide spectrum of disciplines. Hummon and Doreian first introduced this method, and it has since become an effective technique for mapping technological trajectories, exploring scientific knowledge flows, and conducting literature reviews. Nevertheless, there are issues not broadly discussed in applying the method, including the handling of citation data, choosing a proper traversal weight scheme, search options, and interpretation of the resulting paths. This note aims to deepen the discussions and concludes with several suggestions and strategies in applying main path analysis.
Notes
Revision history of Pajek can be found on the website http://mrvar.fdv.uni-lj.si/pajek/history.htm.
The number of references for 4751701 is 22, or much higher than that of its neighbors on the main paths, 5012467, 4560984 and 4598285, which are 10, 7 and 6, respectively.
References
Aksnes, D. W. (2003). A macro study of self-citation. Scientometrics, 56(2), 235–246.
Barberá-Tomás, D., Jiménez-Sáez, F., & Castelló-Molina, I. (2011). Mapping the importance of the real world: The validity of connectivity analysis of patent citations networks. Research Policy, 40(3), 473–486.
Batagelj, V. (2003). Efficient algorithms for citation network analysis. In Preprint series, University of Ljubljana, Institute of Mathematics, Physics and Mechanics, Department of Theoretical Computer Science.
Batagelj, V., Ferligoj, A., & Squazzoni, F. (2017). The emergence of a field: a network analysis of research on peer review. Scientometrics, 113(1), 503–532.
Batagelj, V., & Mrvar, A. (1998). Pajek-program for large network analysis. Connections, 21(2), 47–57.
Bekkers, R., & Martinelli, A. (2012). Knowledge positions in high-tech markets: Trajectories, standards, strategies and true innovators. Technological Forecasting and Social Change, 79(7), 1192–1216.
Bhupatiraju, S., Nomaler, Ö., Triulzi, G., & Verspagen, B. (2012). Knowledge flows: Analyzing the core literature of innovation, entrepreneurship and science and technology studies. Research Policy, 41(7), 1205–1218.
Brysbaert, M., & Smyth, S. (2011). Self-enhancement in scientific research: The self-citation bias. Psychologica Belgica, 51(2), 129–137.
Calero-Medina, C., & Noyons, E. C. (2008). Combining mapping and citation network analysis for a better understanding of the scientific development: The case of the absorptive capacity field. Journal of Informetrics, 2(4), 272–279.
Chuang, T. C., Liu, J. S., Lu, L. Y., Tseng, F.-M., Lee, Y., & Chang, C.-T. (2017). The main paths of eTourism: Trends of managing tourism through Internet. Asia Pacific Journal of Tourism Research, 22(2), 213–231.
Colicchia, C., & Strozzi, F. (2012). Supply chain risk management: A new methodology for a systematic literature review. Supply Chain Management: An International Journal, 17(4), 403–418.
Consoli, D., & Mina, A. (2009). An evolutionary perspective on health innovation systems. Journal of Evolutionary Economics, 19(2), 297.
De Nooy, W., Mrvar, A., & Batagelj, V. (2005). Exploratory social network analysis with Pajek. Cambridge: Cambridge University Press.
De Nooy, W., Mrvar, A., & Batagelj, V. (2018). Exploratory social network analysis with Pajek (3rd ed.). Cambridge: Cambridge University Press.
Epicoco, M. (2013). Knowledge patterns and sources of leadership: Mapping the semiconductor miniaturization trajectory. Research Policy, 42(1), 180–195.
Fontana, R., Nuvolari, A., & Verspagen, B. (2009). Mapping technological trajectories as patent citation networks. An application to data communication standards. Economics of Innovation and New Technology, 18(4), 311–336.
Glänzel, W., Debackere, K., Thijs, B., & Schubert, A. (2006). A concise review on the role of author self-citations in information science, bibliometrics and science policy. Scientometrics, 67(2), 263–277.
Glänzel, W., & Thijs, B. (2004). The influence of author self-citations on bibliometric macro indicators. Scientometrics, 59(3), 281–310.
Harris, J. K., Beatty, K. E., Lecy, J. D., Cyr, J. M., & Shapiro, R. M. (2011). Mapping the multidisciplinary field of public health services and systems research. American Journal of Preventive Medicine, 41(1), 105–111.
Ho, J. C., Saw, E.-C., Lu, L. Y., & Liu, J. S. (2014). Technological barriers and research trends in fuel cell technologies: A citation network analysis. Technological Forecasting and Social Change, 82, 66–79.
Ho, M. H.-C., Liu, J. S., & Chang, K. C.-T. (2017). To include or not: the role of review papers in citation-based analysis. Scientometrics, 110(1), 65–76.
Hummon, N. P., & Doreian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63.
Hung, S.-C., Liu, J. S., Lu, L. Y., & Tseng, Y.-C. (2014). Technological change in lithium iron phosphate battery: The key-route main path analysis. Scientometrics, 100(1), 97–120.
Hyland, K. (2003). Self-citation and self-reference: Credibility and promotion in academic publication. Journal of the American Society for Information Science and Technology, 54(3), 251–259.
Kim, J., & Shin, J. (2018). Mapping extended technological trajectories: integration of main path, derivative paths, and technology junctures. Scientometrics, 116(3), 1439–1459.
Liu, J. S., Chen, H. H., Ho, M. H. C., & Li, Y. C. (2014). Citations with different levels of relevancy: Tracing the main paths of legal opinions. Journal of the Association for Information Science and Technology, 65(12), 2479–2488.
Liu, J. S., & Lu, L. Y. (2012). An integrated approach for main path analysis: Development of the Hirsch index as an example. Journal of the Association for Information Science and Technology, 63(3), 528–542.
Liu, J. S., Lu, L. Y., & Lu, W.-M. (2016). Research fronts in data envelopment analysis. Omega, 58, 33–45.
Liu, J. S., Lu, L. Y., Lu, W.-M., & Lin, B. J. (2013a). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega, 41(1), 3–15.
Liu, J. S., Lu, L. Y., Lu, W.-M., & Lin, B. J. (2013b). A survey of DEA applications. Omega, 41(5), 893–902.
Lu, L. Y., Lan, Y., & Liu, J. S. (2012). A novel approach for exploring technological development trajectories. In: 2012 IEEE International Conference on Management of Innovation and Technology (ICMIT) (pp. 504–509). IEEE.
Lu, L. Y., & Liu, J. S. (2013). An innovative approach to identify the knowledge diffusion path: The case of resource-based theory. Scientometrics, 94(1), 225–246.
Lu, L. Y., & Liu, J. S. (2016). A novel approach to identify the major research themes and development trajectory: The case of patenting research. Technological Forecasting and Social Change, 103, 71–82.
Lucio-Arias, D., & Leydesdorff, L. (2008). Main-path analysis and path-dependent transitions in HistCite™-based historiograms. Journal of the Association for Information Science and Technology, 59(12), 1948–1962.
MacRoberts, M. H., & MacRoberts, B. R. (1989). Problems of citation analysis: A critical review. Journal of the American Society for information Science, 40(5), 342–349.
MacRoberts, M. H., & MacRoberts, B. R. (1996). Problems of citation analysis. Scientometrics, 36(3), 435–444.
Martinelli, A. (2012). An emerging paradigm or just another trajectory? Understanding the nature of technological changes using engineering heuristics in the telecommunications switching industry. Research Policy, 41(2), 414–429.
Mina, A., Ramlogan, R., Tampubolon, G., & Metcalfe, J. S. (2007). Mapping evolutionary trajectories: Applications to the growth and transformation of medical knowledge. Research Policy, 36(5), 789–806.
OuYang, K., Weng, C. S. J. T. F., & Change, S. (2011). A new comprehensive patent analysis approach for new product design in mechanical engineering. Technological Forecasting and Social Change, 78(7), 1183–1199.
Tu, Y. N., & Hsu, S. L. (2016). Constructing conceptual trajectory maps to trace the development of research fields. Journal of the Association for Information Science and Technology, 67(8), 2016–2031.
Verspagen, B. (2007). Mapping technological trajectories as patent citation networks: A study on the history of fuel cell research. Advances in Complex Systems, 10(01), 93–115.
Yeo, W., Kim, S., Lee, J.-M., & Kang, J. (2014). Aggregative and stochastic model of main path identification: A case study on graphene. Scientometrics, 98(1), 633–655.
Acknowledgements
We thank two anonymous reviewers for their constructive comments which have greatly improved the accuracy and readability of this article. This work is partially supported by Taiwan's Ministry of Science and Technology grants MOST 105-2410-H-011-021-MY3, 107-2410-H-155-046, and 106-2410-H-011-028-MY2.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, J.S., Lu, L.Y.Y. & Ho, M.HC. A few notes on main path analysis. Scientometrics 119, 379–391 (2019). https://doi.org/10.1007/s11192-019-03034-x
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-019-03034-x