, Volume 101, Issue 1, pp 291–307 | Cite as

Cext-N index: a network node centrality measure for collaborative relationship distribution



This paper focuses on methods to study the distribution of an author’s collaborative relationships among different communities in co-authorship networks. Based on the index of extensity centrality, we propose a new index and name it extensity centrality-Newman (Cext-N). Drawing upon a data set of three top journals (MISQ, ISR, JMIS) between 2010 and 2012 in Information Systems, we verify and describe the application and value of our approach. Due to the fact that the starting points among Cext-N and classical indices are quite different and a single index is not advocated in scientific evaluation, we can select the indices in actual application by considering their starting points to ensure the value of each index is taken into account.


Centrality measure Co-authorship network Collaborative relationship distribution Lambda sets Community 



This work is partly supported by the National Natural Science Foundation of PRC (Nos. 71172157, 71201039, 71371059 and 71301035) and a grant from the Postdoctoral Science Foundation of China (#2014M550198), and the Fundamental Research Funds for the Central Universities (Grant No. HIT. HSS. 201205).


  1. Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594–607.CrossRefGoogle Scholar
  2. Abbasi, A., Hossaina, L., & Leydesdorff, L. (2012). Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks. Journal of Informetrics, 6(3), 403–412.CrossRefGoogle Scholar
  3. Arenas, A., Cabrales, A., Díaz-Guilera, A., Guimerà, R., & Vega-Redondo, F. (2003). Search and congestion in complex networks. Statistical Mechanics of Complex Networks, 625, 175–194.CrossRefGoogle Scholar
  4. Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2008). Evolution of the social network of scientific collaborations. Physica A, 311(3–4), 590–614.Google Scholar
  5. Barrat, A., Barthélémy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.CrossRefGoogle Scholar
  6. Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 5(10), 1–12.Google Scholar
  7. Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. The Journal of Mathematical Sociology, 2(1), 113–120.CrossRefGoogle Scholar
  8. Bonacich, P., & Lloyd, P. (2001). Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23(3), 191–201.CrossRefGoogle Scholar
  9. Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for windows: Software for social network analysis. Harvard: Analytic Technologies.Google Scholar
  10. Borgatti, S. P., Everett, M. G., & Shirey, P. R. (1990). LS sets, Lambda sets and other cohesive subsets. Social Networks, 12(4), 337–357.MathSciNetCrossRefGoogle Scholar
  11. Börner, K., Dall’asts, L., Ke, W., & Vespignani, A. (2005). Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams. Complexity, 10(4), 57–67.CrossRefGoogle Scholar
  12. Bozzo, E., & Franceschet, M. (2013). Resistance distance, closeness, and betweenness. Social Networks, 35(3), 460–469.CrossRefGoogle Scholar
  13. Cole, B. J. (1981). Dominance hierarchies in Leptothorax ants. Science, 212(4490), 83–84.CrossRefGoogle Scholar
  14. Costa, L. F., Oliveira, O. N., Jr, Travieso, G., Rodrigues, F. A., Boas, P. R. V., Antiqueira, L., et al. (2011). Analyzing and modeling real-world phenomena with complex networks: A survey of applications. Advances in Physics, 60(3), 329–412.CrossRefGoogle Scholar
  15. Dangalchev, C. (2006). Residual closeness in networks. Physica A, 365(2), 556–564.CrossRefGoogle Scholar
  16. Davis, D., Lichtenwalter, R., & Chawla, N. V. (2013). Supervised methods for multirelational link prediction. Social Network Analysis and Mining, 3(2), 127–141.Google Scholar
  17. Dorogovtsev, S. N., & Mendes, J. F. F. (2002). Evolution of networks. Advances in Physics, 51(4), 1079–1187.CrossRefGoogle Scholar
  18. Duch, J., & Arenas, A. (2005). Community detection in complex networks using extremal optimization. Physical Review E, 72(2), 027104.CrossRefGoogle Scholar
  19. Eck, N. J., & Waltman, L. (2009). How to normalize cooccurrence data? An analysis of some well-known similarity measures. Journal of the American Society for Information Science and Technology, 60(8), 1635–1651.CrossRefGoogle Scholar
  20. Fatt, C. K., Ujum, E. A., & Ratnavelu, K. (2010). The structure of collaboration in the Journal of Finance. Scientometrics, 85(3), 849–860.CrossRefGoogle Scholar
  21. Fiala, D., Rousselot, F., & Ježek, K. (2008). PageRank for bibliographic networks. Scientometrics, 76(1), 135–158.CrossRefGoogle Scholar
  22. Freeman, L. C. (1977). A set of measures of centrality based upon betweenness. Sociometry, 40, 35–41.CrossRefGoogle Scholar
  23. Freeman, L. C. (1979). Centrality in social networks conceptual clarification [J]. Social Network, 1(3), 215–239.CrossRefGoogle Scholar
  24. Freeman, L. C., Borgatti, S. P., & White, D. R. (1991). Centrality in valued graphs: A measure of betweenness based on network flow. Social Networks, 13(2), 141–154.MathSciNetCrossRefGoogle Scholar
  25. Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. PNAS, 99(12), 7821–7826.MathSciNetCrossRefMATHGoogle Scholar
  26. Groh, G., & Fuchs, C. (2011). Multi-modal social networks for modeling scientific fields. Scientometrics, 89(2), 569–590.CrossRefGoogle Scholar
  27. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of America, 102(46), 16569–16572.CrossRefGoogle Scholar
  28. Jansen, D., Gortz, R. V., & Heidler, R. (2010). Knowledge production and the structure of collaboration networks in two scientific fields. Scientometrics, 83(1), 219–241.CrossRefGoogle Scholar
  29. Khan, G. F., & Park, H. W. (2013). The e-government research domain: A triple helix network analysis of collaboration at the regional, country, and institutional levels. Government Information Quarterly, 30(2), 182–193.CrossRefGoogle Scholar
  30. Kim, H., & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E, 85(2), 026107.CrossRefGoogle Scholar
  31. Kleinberg, J. M. (1999a). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 604–632.MathSciNetCrossRefMATHGoogle Scholar
  32. Kleinberg, J. M. (1999b). Hubs, authorities, and communities. ACM Computing Surveys, 31(4), 1–3.MathSciNetGoogle Scholar
  33. Liao, C. H. (2011). How to improve research quality? Examining the impacts of collaboration intensity and member diversity in collaboration networks. Scientometrics, 86(3), 741–761.CrossRefGoogle Scholar
  34. Lv, H. Y., & Feng, Y. Q. (2009). A measure of authors’ centrality in co-authorship networks based on the distribution of collaborative relationships. Scientometrics, 81(2), 499–511.CrossRefGoogle Scholar
  35. Newman, M. E. J. (2001a). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E, 64(1), 016132.CrossRefGoogle Scholar
  36. Newman, M. E. J. (2001b). The structure of scientific collaboration networks. PNAS, 98(2), 404–409.CrossRefMATHGoogle Scholar
  37. Newman, M. E. J. (2004). Co-authorship networks and patterns of scientific collaboration. PNAS, 101(1), 5200–5205.CrossRefGoogle Scholar
  38. Newman, M. E. J. (2005). A measure of betweenness centrality based on random walks. Social Networks, 27(1), 39–54.CrossRefGoogle Scholar
  39. Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74(3), 1–22.CrossRefGoogle Scholar
  40. Newman, M. E. J. (2010). Networks: An introduction. Oxford University Press, 167–169, 183.Google Scholar
  41. Noh, J. D., & Rieger, H. (2004). Random walks on complex networks. Physics Review Letters, 92(11), 118701.1–118701.4.CrossRefGoogle Scholar
  42. Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.CrossRefGoogle Scholar
  43. Opsahl, T., Colizza, V., Panzarasa, P., & Ramasco, J. J. (2008). Prominence and control: The weighted rich-club effect. Physical Review Letters, 101, 168702.CrossRefGoogle Scholar
  44. Page, L., Brin, S., Motwani, R., & Winograd. T. (1998). The PageRank citation ranking: Bringing order to the web. Technical Report. Stanford InfoLab.Google Scholar
  45. Park, S., Park, M., Kim, H., Kim, H., Yoon, W., Yoon, T. B., et al. (2013). A closeness centrality analysis algorithm for workflow-supported social networks. In 2013 15th International conference on advanced communication technology (ICACT) (pp. 158–161).Google Scholar
  46. Sabidussi, G. (1966). The centrality index of a graph. Psychomatrika, 31(4), 581–603.MathSciNetCrossRefMATHGoogle Scholar
  47. Salton, G., & Mcgill, M. J. (1983). Introduction to modern information retrieval. New York: McGraw-Hill.MATHGoogle Scholar
  48. Sekercioglu, C. H. (2008). Quantifying coauthor contributions. Science, 322, 371.CrossRefGoogle Scholar
  49. Souza, C. G., & Ferreira, M. L. A. (2013). Researchers profile, co-authorship pattern and knowledge organization in information science in Brazil. Scientometrics, 95(2), 673–687.CrossRefGoogle Scholar
  50. Stephenson, K., & Zelen, M. (1989). Rethinking centrality: Methods and examples. Social Networks, 11(1), 1–37.MathSciNetCrossRefGoogle Scholar
  51. Tutzauer, F. (2007). Entropy as a measure of centrality in networks characterized by path-transfer flow. Social Networks, 29(2), 249–265.CrossRefGoogle Scholar
  52. Wehmuth, K., & Ziviani, A. (2013). DACCER: Distributed Assessment of the Closeness Centrality Ranking in complex networks. Computer Networks, 57(13), 2536–2548.CrossRefGoogle Scholar
  53. Yamashita, Y., & Okubo, Y. (2006). Patterns of scientific collaboration between Japan and France: Inter-sectoral analysis using Probabilistic Partnership Index (PPI). Scientometrics, 68(2), 303–324.Google Scholar
  54. Yan, E., & Ding, Y. (2009). Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the American Society for Information Science and Technology, 60(10), 2107–2118.CrossRefGoogle Scholar
  55. Yan, X. B., Zhai, L., & Fan, W. G. (2013). C-index: A weighted network node centrality measure for collaboration competence. Journal of Informetrics, 7(1), 223–239.CrossRefGoogle Scholar
  56. Yin, L., Kretschmer, H., Hannemann, R. A., & Liu, Z. (2006). Connection and stratification in research collaboration: An analysis of the COLLNET network. Information Processing and Management, 42(6), 1599–1613.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2014

Authors and Affiliations

  1. 1.School of ManagementHarbin Institute of TechnologyHarbinChina

Personalised recommendations