Abstract
Profuse growth of scientometrics as a research field owes a discernible attribution to the introduction of citation networks and other scientograms. Centrality analysis, path analysis and cluster analysis are three major network analysis tools. Hummon and Doreian’s introduction of path retrieval methods based on (1) traversal count as weight assignment (for arcs) method and (2) search methods such as local (forward) search and global search, marked the commencement of path analysis. Original Hummon–Doreian traversal count based weight assignment methods such as Search Path Link Count and Search Path Node Pair were computationally complex. Along with the computational improvement of these weights, Batagelj added another computationally efficient traversal count method to the path analysis literature known as Search Path Count. A major development in search methods was seen recently with the introduction of innovative search methods such as backward (local) search and key-route (local and global) search by Liu and Lu. They also powered the available and new local search methods with a parameter to control the search. Major advantage of Liu–Lu methods lies in the fact that these can reveal more paths or more papers that are usually missed out in classical methods. All these contributions considered unweighted citation networks as the object of analysis. Despite being a tool of tremendous potential, path analysis is much underexplored relative to other network analysis tools. Inspired by these, we generalise Liu–Lu integrated approach, the present state-of-art in path analysis to an integrated approach for weighted networks. We demonstrate a manifold improvement in analysis opportunities with the generalized integrated approach using FV gradient weights for weight assignment, on a case study of the field ‘IT for engineering’. Integrated approach for weighted networks do not need additional implementation effort in PAJEK and this will be beneficial for a multitude of analysts and decision makers.
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References
Batagelj, V. (2003). Efficient algorithms for citation network analysis. arXiv preprint cs/0309023.
Batagelj, V., & Mrvar, A. (1998). Pajek-program for large network analysis. Connections, 21(2), 47–57.
Bilke, S., & Peterson, C. (2001). Topological properties of citation and metabolic networks. Physical Review E, 64(3), 036106.
Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.
Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71.
Brughmans, T. (2013). Networks of networks: A citation network analysis of the adoption, use, and adaptation of formal network techniques in archaeology. Literary and Linguistic Computing, 28(4), 538–562.
Calero-Medina, C., & Noyons, E. C. M. (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.
Chen, Y.-B., Liu, J. S., & Lin, P. (2013). Recent trend in graphene for optoelectronics. Journal of Nanoparticle Research, 15(2), 1454.
Chuang, T. C., Liu, J. S., Lu, L. Y. Y., & Lee, Y. (2014). The main paths of medical tourism: From transplantation to beautification. Tourism Management, 45, 49–58.
De Solla Price, D. J. (1965). Networks of scientific papers. Science, 149, 510–515.
Egghe, L., & Rousseau, R. (2002). Co-citation, bibliographic coupling and a characterization of lattice citation networks. Scientometrics, 55(3), 349–361.
Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science, 122(3159), 108–111.
Garfield, E., Sher, I. H., & Torpie, R. J. (1964). The use of citation data in writing the history of science. Technical report, Institute for Scientific Information Inc., Philadelphia, PA.
Garner, R. (1967). A computer oriented, graph theoretic analysis of citation index structures. In In B. Flood (Ed.), Three Drexel information science research studies (pp. 3–46).
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417.
Hummon, N. P., & Doreian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63.
Jo, S. J., Jeung, C.-W., Park, S., & Yoon, H. J. (2009). Who is citing whom: Citation network analysis among hrd publications from 1990 to 2007. Human Resource Development Quarterly, 20(4), 503–537.
Kaffash, S., & Marra, M. (2017). Data envelopment analysis in financial services: A citations network analysis of banks, insurance companies and money market funds. Annals of Operations Research, 253(1), 307–344.
Karunan, K., Lathabai, H. H., & Prabhakaran, T. (2017). Discovering interdisciplinary interactions between two research fields using citation networks. Scientometrics, 113(1), 335–367.
Lathabai, H. H., Prabhakaran, T., & Changat, M. (2015). Centrality and flow vergence gradient based path analysis of scientific literature: A case study of biotechnology for engineering. Physica A: Statistical Mechanics and its Applications, 429, 157–168.
Lathabai, H. H., Prabhakaran, T., & Changat, M. (2017). Contextual productivity assessment of authors and journals: A network scientometric approach. Scientometrics, 110(2), 711–737.
Leydesdorff, L, Wagner, C. S., & Bornmann, L. (2017). Betweenness and diversity in journal citation networks as measures of interdisciplinarity: A tribute to eugene garfield. Scientometrics.
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. 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. Y., Lu, W.-M., & Lin, B. J. Y. (2013). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega, 41(1), 3–15.
Martinelli, A., & Nomaler, Ö. (2014). Measuring knowledge persistence: A genetic approach to patent citation networks. Journal of Evolutionary Economics, 24(3), 623–652.
Merton, R. K. (1988). The matthew effect in science, ii: Cumulative advantage and the symbolism of intellectual property. ISIS, 79(4), 606–623.
Merton, R. K. (1965). On the shoulders of giants: A Shandean postscript. San Diego: Harcourt.
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.
Moya-Anegón, S. G. F. D., Vargas-Quesada, B., Chinchilla-Rodríguez, Z., Corera-Álvarez, E., Munoz-Fernández, F. J., & Herrero-Solana, V. (2007). Visualizing the marrow of science. Journal of the Association for Information Science and Technology, 58(14), 2167–2179.
Newman, M. E. J. (2008). The mathematics of networks. The New Palgrave Encyclopedia of Economics, 2(2008), 1–12.
Prabhakaran, T., Lathabai, H. H., & Changat, M. (2015). Detection of paradigm shifts and emerging fields using scientific network: A case study of information technology for engineering. Technological Forecasting and Social Change, 91, 124–145.
Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5(3), 269–287.
Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2009). Comparative study on methods of detecting research fronts using different types of citation. Journal of the Association for Information Science and Technology, 60(3), 571–580.
Shibata, N., Kajikawa, Y., Takeda, Y., Sakata, I., & Matsushima, K. (2011). Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications. Technological Forecasting and Social Change, 78(2), 274–282.
Tampubolon, G., & Ramlogan, R. (2007). Networks and temporality in the development of a radical medical treatment. Graduate Journal of Social Science, 4(1), 54–77.
Valverde, S., Solé, R. V., Bedau, M. A., & Packard, N. (2007). Topology and evolution of technology innovation networks. Physical Review E, 76(5), 056118.
Xiao, Y., Lu, L. Y. Y., Liu, J. S., & Zhou, Z. (2014). Knowledge diffusion path analysis of data quality literature: A main path analysis. Journal of Informetrics, 8(3), 594–605.
Acknowledgements
Authors are grateful to Govt. of Kerala, for funding this work under Innovative programmmes/Research projects scheme, State Plan Grant (No. Pl.A1/1074/DFS/17). This work used the facility provided by ‘Scientometric lab’ (order No.Pl.A1/Annual plan 16-17/Imp.plan/16 dtd. 29/11/2016), Department of Futures Studies, University of Kerala.
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Lathabai, H.H., George, S., Prabhakaran, T. et al. An integrated approach to path analysis for weighted citation networks. Scientometrics 117, 1871–1904 (2018). https://doi.org/10.1007/s11192-018-2917-1
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DOI: https://doi.org/10.1007/s11192-018-2917-1