Abstract
In research community, who are the most prominent or key authors in the research community is the major discussion or research issue. Different types of centrality measures and citation based indices are developed for finding key author in community. But main issues is what are the real contribution of an individual or group and their impact in research community. To find contribution of individual researcher, we use normalized citation count and geometric series to distribute the share to individual author in multi-authored paper. For evaluating the scientific impact of individual researcher, we use eigenvector centrality. In eigenvector centrality first, we set the initial amount of influence of each author to total normalized citation score and the collaboration weight is correlation coefficient value.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abbasi, A., Altmann, J.: On the correlation between research performance and social network analysis measures applied to research collaboration networks. In: 44th Hawaii International Conference on System Sciences (HICSS), 2011, pp. 1–10. IEEE (2011)
Bihari, A., Pandia, M.K.: Key author analysis in research professionals relationship network using citation indices and centrality. Proc. Comput. Sci. 57, 606–613 (2015)
Pandia, M.K., Bihari, A.: Important author analysis in research professionals relationship network based on social network analysis metrics. In: Computational Intelligence in Data Mining, vol. 3, pp. 185–194. Springer (2015)
Newman, M.E.: Scientific collaboration networks. I. Network construction and fundamental results. Phys. Rev. E 64(1), 016131 (2001)
Farkas, I., Bel, D., Palla, G., Vicsek, T.: Weighted network modules. New J. Phys. 9(6), 180 (2007). http://stacks.iop.org/1367-2630/9/i=6/a=180
Abbasi, A., Hossain, L., Uddin, S., Rasmussen, K.J.: Evolutionary dynamics of scientific collaboration networks: multi-levels and cross-time analysis. Scientometrics 89(2), 687–710 (2011)
Wang, B., Yao, X.: To form a smaller world in the research realm of hierarchical decision models. In: International Conference on Industrial Engineering and Engineering Management (IEEM), 2011 IEEE, pp. 1784–1788. IEEE (2011)
Liu, X., Bollen, J., Nelson, M.L., Van de Sompel, H.: Co-authorship networks in the digital library research community. Inf. Process. Manage. 41(6), 1462–1480 (2005)
Liu, J., Li, Y., Ruan, Z., Fu, G., Chen, X., Sadiq, R., Deng, Y.: A new method to construct co-author networks. Phys. A Stat. Mech. Appl. 419, 29–39 (2015)
Bihari, A., Pandia, M.K.: Eigenvector centrality and its application in research professionals’ relationship network. In: 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), pp. 510–514. IEEE (2015)
Umadevi, V.: Automatic co-authorship network extraction and discovery of central authors. Int. J. Comput. Appl. 74(4), 1–6 (2013)
Jin, J., Xu, K., Xiong, N., Liu, Y., Li, G.: Multi-index evaluation algorithm based on principal component analysis for node importance in complex networks. IET Netw. 1(3), 108–115 (2012)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press (1994)
Liu, B.: Web Data Mining. Springer (2007)
Said, Y.H., Wegman, E.J., Sharabati, W.K., Rigsby, J.T.: Retracted: social networks of author-coauthor relationships. Comput. Stat. Data Anal. 52(4), 2177–2184 (2008)
Bonacich, P., Lloyd, P.: Eigenvector-like measures of centrality for asymmetric relations. Soc. Netw. 23(3), 191–201 (2001)
Newman, M.E.: The mathematics of networks. New Palgrave Encycl. Econ. 2, 1–12 (2008)
Ding, D.-W., He, X.-Q.: Application of eigenvector centrality in metabolic networks. In: 2nd International Conference on Computer Engineering and Technology (ICCET), 2010, vol. 1, pp. V1–89. IEEE (2010)
Wang, B., Yang, J.: To form a smaller world in the research realm of hierarchical decision models. In: Proceedings of PICMET’11. PICMET (2011)
Schult, D.A., Swart, P.: Exploring network structure, dynamics, and function using networkX. In: Proceedings of the 7th Python in Science Conference (SCIPY 2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Bihari, A., Tripathi, S. (2017). A New Method for Key Author Analysis in Research Professionals’ Collaboration Network. In: Chaki, R., Saeed, K., Cortesi, A., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 567. Springer, Singapore. https://doi.org/10.1007/978-981-10-3409-1_9
Download citation
DOI: https://doi.org/10.1007/978-981-10-3409-1_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3408-4
Online ISBN: 978-981-10-3409-1
eBook Packages: EngineeringEngineering (R0)