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An integrated approach to path analysis for weighted citation networks

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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.

    MATH  Google Scholar 

  • Bilke, S., & Peterson, C. (2001). Topological properties of citation and metabolic networks. Physical Review E, 64(3), 036106.

    Article  Google Scholar 

  • Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113–120.

    Article  Google Scholar 

  • Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71.

    Article  MathSciNet  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chen, Y.-B., Liu, J. S., & Lin, P. (2013). Recent trend in graphene for optoelectronics. Journal of Nanoparticle Research, 15(2), 1454.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • De Solla Price, D. J. (1965). Networks of scientific papers. Science, 149, 510–515.

    Article  Google Scholar 

  • Egghe, L., & Rousseau, R. (2002). Co-citation, bibliographic coupling and a characterization of lattice citation networks. Scientometrics, 55(3), 349–361.

    Article  Google Scholar 

  • Garfield, E. (1955). Citation indexes for science: A new dimension in documentation through association of ideas. Science, 122(3159), 108–111.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hummon, N. P., & Doreian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Karunan, K., Lathabai, H. H., & Prabhakaran, T. (2017). Discovering interdisciplinary interactions between two research fields using citation networks. Scientometrics, 113(1), 335–367.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lathabai, H. H., Prabhakaran, T., & Changat, M. (2017). Contextual productivity assessment of authors and journals: A network scientometric approach. Scientometrics, 110(2), 711–737.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Martinelli, A., & Nomaler, Ö. (2014). Measuring knowledge persistence: A genetic approach to patent citation networks. Journal of Evolutionary Economics, 24(3), 623–652.

    Article  Google Scholar 

  • Merton, R. K. (1988). The matthew effect in science, ii: Cumulative advantage and the symbolism of intellectual property. ISIS, 79(4), 606–623.

    Article  Google Scholar 

  • Merton, R. K. (1965). On the shoulders of giants: A Shandean postscript. San Diego: Harcourt.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Newman, M. E. J. (2008). The mathematics of networks. The New Palgrave Encyclopedia of Economics, 2(2008), 1–12.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Seidman, S. B. (1983). Network structure and minimum degree. Social Networks, 5(3), 269–287.

    Article  MathSciNet  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Valverde, S., Solé, R. V., Bedau, M. A., & Packard, N. (2007). Topology and evolution of technology innovation networks. Physical Review E, 76(5), 056118.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

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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Correspondence to Thara Prabhakaran.

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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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