Abstract
We apply network analysis to study bipartite consumer- service graph that represents service transaction to understand consumer demand. Based on real-world computer log files of a library, we found that consumer graph projected from bipartite graph deviates significantly from theoretical predictions based on random bipartite graph. We observed smaller-than-expected average degree, larger-than-expected average path length and stronger-than-expected tendency to cluster. These findings motivated to explore the community structure of the network. As a result, the weighted consumer network showed significant community structure than the unweighted network. Communities picked out by the algorithm revealed that individuals in the same community were due to their common specialties or the overlapping structure of knowledge between their specialties.
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References
Amaral, L.A.N., Uzzi, B.: Complex Systems-A New Paradigm for the Integrative Study of Management, Physical, and Technological Systems. Management Science 53, 1033–1035 (2007)
Epstein, J.M., Axtell, R.: Growing Artificial Societies: Social Science from the Bottom Up. The MIT Press, Cambridge (1996)
Wolfram, S.: A New Kind of Science. Wolfram Media, Champaign (2002)
Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)
Huang, Z., Zeng, D.D., Chen, H.: Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems. Management Science 53, 1146–1164 (2007)
Newman, M.E.J., Strogatz, S.H., Watts, D.J.: Random graphs with arbitrary degree distributions and their applications. Phys. Rev. EÂ 64, 26118 (2001)
Nooy, W., de Mrvar, A., Batagelj, V.: Exploratory Social Network Analysis with Pajek. Cambridge University Press, Cambridge (2005)
Zhou, T., Ren, J., Medo, M., Zhang, Y.C.: Bipartite network projection and personal recommendation. Phys. Rev. EÂ 76, 046115 (2007)
Zhang, P.P., Chen, K., He, Y., Zhou, T., Su, B.B., Jin, Y.D., Chang, H., Zhou, Y.P., Sun, L.C., Wang, B.H., He, D.R.: Model and empirical study on some collaboration networks. Phys. A 360(2), 599–616 (2006)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821–7826 (2002)
Girvan, M., Newman, M.E.J.: Finding and evaluating community structure in networks. Phys. Rev. EÂ 69, 026113 (2004)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. EÂ 69, 066133 (2004)
Newman, M.E.J.: Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys. Rev. EÂ 64, 016132 (2001)
Newman, M.E.J.: Scientific collaboration networks. I. Network construction and fundamental results. Phys. Rev. EÂ 64, 016131 (2001)
Gleiser, P., Danon, L.: Community structure in jazz. Preprint cond-mat/0307434 (2003)
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© 2009 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Li, L., Gu, BY., Chen, L. (2009). The Topological Characteristics and Community Structure in Consumer-Service Bipartite Graph. In: Zhou, J. (eds) Complex Sciences. Complex 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02466-5_62
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DOI: https://doi.org/10.1007/978-3-642-02466-5_62
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