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.
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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.
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.
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.
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.
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.
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.
Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. The Journal of Mathematical Sociology, 2(1), 113–120.
Bonacich, P., & Lloyd, P. (2001). Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23(3), 191–201.
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET for windows: Software for social network analysis. Harvard: Analytic Technologies.
Borgatti, S. P., Everett, M. G., & Shirey, P. R. (1990). LS sets, Lambda sets and other cohesive subsets. Social Networks, 12(4), 337–357.
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.
Bozzo, E., & Franceschet, M. (2013). Resistance distance, closeness, and betweenness. Social Networks, 35(3), 460–469.
Cole, B. J. (1981). Dominance hierarchies in Leptothorax ants. Science, 212(4490), 83–84.
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.
Dangalchev, C. (2006). Residual closeness in networks. Physica A, 365(2), 556–564.
Davis, D., Lichtenwalter, R., & Chawla, N. V. (2013). Supervised methods for multirelational link prediction. Social Network Analysis and Mining, 3(2), 127–141.
Dorogovtsev, S. N., & Mendes, J. F. F. (2002). Evolution of networks. Advances in Physics, 51(4), 1079–1187.
Duch, J., & Arenas, A. (2005). Community detection in complex networks using extremal optimization. Physical Review E, 72(2), 027104.
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.
Fatt, C. K., Ujum, E. A., & Ratnavelu, K. (2010). The structure of collaboration in the Journal of Finance. Scientometrics, 85(3), 849–860.
Fiala, D., Rousselot, F., & Ježek, K. (2008). PageRank for bibliographic networks. Scientometrics, 76(1), 135–158.
Freeman, L. C. (1977). A set of measures of centrality based upon betweenness. Sociometry, 40, 35–41.
Freeman, L. C. (1979). Centrality in social networks conceptual clarification [J]. Social Network, 1(3), 215–239.
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.
Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. PNAS, 99(12), 7821–7826.
Groh, G., & Fuchs, C. (2011). Multi-modal social networks for modeling scientific fields. Scientometrics, 89(2), 569–590.
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.
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.
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.
Kim, H., & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E, 85(2), 026107.
Kleinberg, J. M. (1999a). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 604–632.
Kleinberg, J. M. (1999b). Hubs, authorities, and communities. ACM Computing Surveys, 31(4), 1–3.
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.
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.
Newman, M. E. J. (2001a). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E, 64(1), 016132.
Newman, M. E. J. (2001b). The structure of scientific collaboration networks. PNAS, 98(2), 404–409.
Newman, M. E. J. (2004). Co-authorship networks and patterns of scientific collaboration. PNAS, 101(1), 5200–5205.
Newman, M. E. J. (2005). A measure of betweenness centrality based on random walks. Social Networks, 27(1), 39–54.
Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74(3), 1–22.
Newman, M. E. J. (2010). Networks: An introduction. Oxford University Press, 167–169, 183.
Noh, J. D., & Rieger, H. (2004). Random walks on complex networks. Physics Review Letters, 92(11), 118701.1–118701.4.
Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.
Opsahl, T., Colizza, V., Panzarasa, P., & Ramasco, J. J. (2008). Prominence and control: The weighted rich-club effect. Physical Review Letters, 101, 168702.
Page, L., Brin, S., Motwani, R., & Winograd. T. (1998). The PageRank citation ranking: Bringing order to the web. Technical Report. Stanford InfoLab.
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).
Sabidussi, G. (1966). The centrality index of a graph. Psychomatrika, 31(4), 581–603.
Salton, G., & Mcgill, M. J. (1983). Introduction to modern information retrieval. New York: McGraw-Hill.
Sekercioglu, C. H. (2008). Quantifying coauthor contributions. Science, 322, 371.
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.
Stephenson, K., & Zelen, M. (1989). Rethinking centrality: Methods and examples. Social Networks, 11(1), 1–37.
Tutzauer, F. (2007). Entropy as a measure of centrality in networks characterized by path-transfer flow. Social Networks, 29(2), 249–265.
Wehmuth, K., & Ziviani, A. (2013). DACCER: Distributed Assessment of the Closeness Centrality Ranking in complex networks. Computer Networks, 57(13), 2536–2548.
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.
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.
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.
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.
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).
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Zhang, G., Liu, L., Feng, Y. et al. Cext-N index: a network node centrality measure for collaborative relationship distribution. Scientometrics 101, 291–307 (2014). https://doi.org/10.1007/s11192-014-1358-8
- Centrality measure
- Co-authorship network
- Collaborative relationship distribution
- Lambda sets