Back
pdf
Journal article

An evolving model of online bipartite networks

  • Zhang, Chu-Xu Institute of Information Economy, Hangzhou Normal University, China - Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, China
  • Zhang, Zi-Ke Institute of Information Economy, Hangzhou Normal University, China - Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, China - Department of Physics, University of Fribourg, Switzerland
  • Liu, Chuang Institute of Information Economy, Hangzhou Normal University, China
    01.12.2013
Published in:
  • Physica A: Statistical Mechanics and its Applications. - 2013, vol. 392, no. 23, p. 6100–6106
Bipartite networks
Evolving model
Network dynamics
English 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.
Faculty
Faculté des sciences et de médecine
Department
Département de Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/303474
Statistics
Document views: 29 File downloads:
  • zha_emo.pdf: 41