Zhang, Chu-Xu Zhang, Zi-Ke Liu, Chuang 2013-12-01 Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law , which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike , show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter pp, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of pp. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks. application/pdf https://folia.unifr.ch/global/documents/303474 https://folia.unifr.ch/documents/303474/files/zha_emo.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2013.07.027 info:eu-repo/semantics/openAccess License undefined Physica A: Statistical Mechanics and its Applications. - 2013, vol. 392, no. 23, p. 6100–6106 Bipartite networks Evolving model Network dynamics info:eu-repo/classification/udc/53 An evolving model of online bipartite networks http://purl.org/coar/resource_type/c_6501