Important Author Analysis in Research Professionals’ Relationship Network Based on Social Network Analysis Metrics

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)

Abstract

Important author analysis is one of the key issues in the research professionals’ relationship network. Research professionals’ relationship network is a type of social network which is constitute of research professionals and there co-author relationship with other professionals. So many social network analysis metrics are available to analyze the important or prominent actor in the network. Centrality in social network analysis represents prestige or importance of a node with respect to other nodes in the network and also represents the importance of relationship between nodes. In this paper, we studied social network theory to understand how the collaboration of research professionals has impact in research world and performance of individual researcher. For this analysis, we use social network analysis metrics like normalize degree centrality, closeness centrality, betweenness centrality and eigenvector centrality.

Keywords

Social network Researcher relation Centrality 

References

  1. 1.
    Liu, B.: Web Data Mining. Springer International Edition, New York (2006)Google Scholar
  2. 2.
    Alireza Abbasi, J.A.: On the correlation between research performance and social network analysis measure applied to scholars collaboration networks, In: Proceedings HICSS (2011)Google Scholar
  3. 3.
    Liu, X., Bollen, J., Nelson, L.M., Sompel, H.: Co-authorship networks in the digital library research community, information processing and management. Int. Process. Manag. 41, 1462–1480 (2005)Google Scholar
  4. 4.
    Jie Tang, D.Z., Yao, L.: Social network extraction of academic researchers. In: IEEE International Conference on Data Mining (ICDM) (2007)Google Scholar
  5. 5.
    Said, E.J.W.Y.H., Sharabati, W.K.: Retracted: social networks of author coauthor relationships. Comput. Stat. Data Anal. 52, 2177–2184 (2005)Google Scholar
  6. 6.
    Carlos, D., Correa, T.C., Ma, K.-L.: Visual reasoning about social networks using centrality, sensitivity. IEEE Trans. Vis. Comput. Graph. 18(1) (2012)Google Scholar
  7. 7.
    Landherr, A., Friedl, B., Heidemann, J.: A critical review of centrality measures in social networks. Bus. Inf. Syst. Eng. 2(6), 371–385 (2010)CrossRefGoogle Scholar
  8. 8.
    Wasserman, S., Faust, K.: Social network analysis: methods and applications. Cambridge University Press, New York (1994)Google Scholar
  9. 9.
    Miray Kas, L.R.C., Carley, K.M.: Monitoring social centrality for peer-to-peer network protection. IEEE Commun. Mag. 51, 151–161 (2013)Google Scholar
  10. 10.
    Friedl, B., Landherr, A., Heidemann, J.: A critical review of centrality measures in social networks. BISE—STATE OF THE ART 371–385 (2009)Google Scholar
  11. 11.
    Shams-ul Arfeen, J.M., Kazi, A., Hyder, S.: Automatic co-authorship network extraction and discovery of central authors. Int. J. Comput. Appl. (0975 8887) 74(4), 1–6 (2013)Google Scholar
  12. 12.
    Deng, Q., Wang, Z.: Degree centrality in scientific collaboration supernetwork. In: International Conference on Information Science and Technology, pp. 259–262. 26–28 March 2011Google Scholar
  13. 13.
    Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)Google Scholar
  14. 14.
    Borgatti, S.P.: Centrality and AIDS. Connections 18(1), 112–114Google Scholar
  15. 15.
    Jin, J., Liu, Y., Xu, K., Xiong, N., Li, G.: Multi-index evaluation algorithm based on principal component analysis for node importance in complex networks. Inst. Eng. Technol. 1(3), 108–115 (2012)Google Scholar
  16. 16.
    Carlos, K.-L.M., Correa, D., Crnovrsanin, T., Keeton, K.: The derivatives of centrality and their applications in visualizing social networksGoogle Scholar
  17. 17.
    Borgatti, S.P., Everett, M.G.: A graph-theoretic perspective on centrality. Elsevier Soc. Netw. 1–19 (2005)Google Scholar
  18. 18.
    Daniel, G., Figueira, J.R., Eusébio, A.: Modeling centrality measures in social network analysis using bi-criteria network flow optimization problems. Eur. J. Oper. Res. 226, 354–365 (2013) Google Scholar
  19. 19.
    Borgatti, S.P.: Centrality and network flow. Soc. Netw. 27(1), 55–71 (2005)CrossRefGoogle Scholar
  20. 20.
    Estrada, E., Rodríguez-Velázquez, J.A.: Subgraph centrality in complex networks, 1–29Google Scholar
  21. 21.
    Wang, G., Shen, Y., Luan, E.: A measure of centrality based on modularity matrix. Prog. Nat. Sci. 18, 104–1043 (2008) (Elsevier)Google Scholar
  22. 22.
    Lohmann, G., Margulies, D.S., Horstmann, A., Pleger, B., Lepsien, J., Goldhahn, D., Schloegl, H., Stumvoll, M., Villringer, A., Turner, R.: Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain. PLoS ONE 5(4), 1–8 e10232 (2010). doi: 10.1371/journal.pone.0010232
  23. 23.
    Spizzirri, L.: Justification and application of eigenvector centrality. Algebra in Geography: Eigenvectors of Network (2011)Google Scholar
  24. 24.
    Phillip Bonacich, P.L.: Eigenvector-like measures of centrality for asymmetric relations. Soc. Netw. 23, 191–201 (2001) (Elsevier Press)Google Scholar
  25. 25.
    Newman, M.E.J.: The mathematics of networksGoogle Scholar
  26. 26.
    Ding, D., He, X.: Application of eigenvector centrality in metabolic networks. In: 2nd International Conference on Computer Engineering and Technology, vol. 1 (2010)Google Scholar
  27. 27.
    Straffin, P.D. Jr.: Linear algebra in geography: eigenvectors of networks. JSTOR Math. Mag. 53, 269–276 (1980)Google Scholar
  28. 28.
    Kato, Y., Ono, F.: Node centrality on disjoint multipath routing. IEEE (2011)Google Scholar
  29. 29.
  30. 30.
    Aric, D.A.S., Hagberg, A., Swart, P.J.: Exploring network structure, dynamics, and function using network. In: Proceedings of the 7th Python in Science Conference (scipy 2008) (2008)Google Scholar
  31. 31.
    Wang, B., Yang, J.: To form a smaller world in the research realm of hierarchical decision models. In: Proceedings PICMET’11 (2011)Google Scholar
  32. 32.
    Wang, B., Yao, X.: To form a smaller world in the research realm of hierarchical decision models. In: Proceedings of the 2011 IEEE IEEM’, pp. 1784–1788 (2011)Google Scholar
  33. 33.
    Yulan, C.H.S.H.: Mining a web citation database for author co-citation analysis. Inf. Process. Manage. 38, 491–508 (2002)Google Scholar
  34. 34.
    Newman, M.E.J.: Coauthorship networks and patterns of scientific collaboration. In: Proceedings of the National Academy of the United States of America (2004)Google Scholar
  35. 35.
    Said, Y.H., Wegman, E.J., Sharabati, W.K.: RETRACTED: social networks of author-coauthor relationships. Comput. Stat. Data Anal. 52(4), 2177–2184 (2008)CrossRefMathSciNetGoogle Scholar
  36. 36.
    Yulan, H., Siu, C. H.: Mining a web citation database for author co-citation analysis. Inf. Process. Manage. 38(4), 491–508 2002Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of MCASilicon Institute of TechnologyBhubaneswarIndia
  2. 2.Department of CSESilicon Institute of TechnologyBhubaneswarIndia

Personalised recommendations