AJIBM  Vol.5 No.4 , April 2015
Methods of Measuring Influence of Bank Customer Using Social Network Model
Abstract: As the development of economic today, Bank business deeply integrates into our lives. Thousands of transactions generate the large amounts of data. Traditional bank customer management system simply classifies and counts these data, and the system is deficient in customer relationship management. However, the social network model can provide the influence between the bank customers for the bank. In this paper, we construct a social network by calculating the relationship between bank customers. In order to explore how bank customers in the bank customer network affect each other, we use three indicators: the average path length L, clustering coefficient C and degree distribution p (x) to conduct a comprehensive analysis. Consequently, we find that there evidently exist influential customers in this network.
Cite this paper: Mao, H. , Jin, X. and Zhu, L. (2015) Methods of Measuring Influence of Bank Customer Using Social Network Model. American Journal of Industrial and Business Management, 5, 155-160. doi: 10.4236/ajibm.2015.54017.

[1]   Luo, Y.H. (2009) Research and Application of Scale-Free Network Model. Master Thesis, Donghua University, Shanghai.

[2]   Ding, S.M. (2012) Social Network Analysis Methods Coauthored Network Applications for Example. Master Thesis, Tianjin Normal University, Tianjin.

[3]   Tao, N.H. and Zhao, X. (2009) The Center of The Indicators and Improvement in the Use of Journal Citation Network Analysis. Library and Information Work, 53, 7-144.

[4]   Li, L. (2010) Co-Authored Research Papers and Research Cooperation between Network Structures. Master Thesis, Jilin University, Jilin.

[5]   Du, H.F., Li, Z., et al. (2007) Community Structure in Small-World Networks and Scale-Free Networks. Physics, 11, 431-434.

[6]   Newman, M.E. (2001) The Structure of Scientific Collaboration Networks. Proceedings of the National Academy of Sciences, 98, 404-409.

[7]   Newman, M.E. (2001) Scientific Collaboration Networks. I. Network Construction and Fundamental Results. Physical review E, 64, 8 p.

[8]   Geng, Z.J. and Wang, W.D. (2009) Analysis of the Cause of the Power-Law of Citation Network. Journal of Intelligence, 28, 15-17.

[9]   Zuo, M.Y., Wen, X.W. and Hua, X.Q. (2012) Analysis on Co-Authorship Network of Scholars in Knowledge Management. Journal of Information Resources Management, 4, 4-15.

[10]   Erdös, P. and Rényi, A. (1960) On the Evolution of Random Graphs. Publication of the Mathematical Institute of the Hungarian Academy of Sciences, 5, 17-61.

[11]   Albert, R., Jeong, H. and Barabási, A.L. (1999) Internet: Diameter of the World-Wide Web. Nature, 401, 130-131.

[12]   Barabási, A.L. and Albert, R. (1999) Emergence of Scaling in Random Networks. Science, 286, 509-512.

[13]   Otte, E. and Rousseau, R. (2002) Social Network Analysis: A Powerful Strategy, Also for the Information Sciences. Journal of Information Science, 28, 441-453.

[14]   Bar-Ilan, J. (2008) Informetrics at the Beginning of the 21st Century—A Review. Journal of Informetrics, 2, 1-52.

[15]   Everett, M.G., Sinclair, P. and Dankelmann, P. (2004) Some Centrality Results New and Old. Journal of Mathematical Sociology, 28, 215-227.

[16]   Carrington, P.J., Scott, J. and Wasserman, S. (2005) Models and Methods in Social Network Analysis. No. 28, Cambridge University Press, Cambridge.

[17]   Freeman, L.C. (1979) Centrality in Social Networks Conceptual Clarification. Social Networks, 1, 215-239.