Modeling one-mode projection of bipartite networks by tagging vertex information

Jian Qiao, Ying Ying Meng, Hsinchun Chen, Hong Qiao Huang, Guo Ying Li

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Traditional one-mode projection models are less informative than their original bipartite networks. Hence, using such models cannot control the projection's structure freely. We proposed a new method for modeling the one-mode projection of bipartite networks, which thoroughly breaks through the limitations of the available one-mode projecting methods by tagging the vertex information of bipartite networks in their one-mode projections. We designed a one-mode collaboration network model by using the method presented in this paper. The simulation results show that our model matches three real networks very well and outperforms the available collaboration network models significantly, which reflects the idea that our method is ideal for modeling one-mode projection models of bipartite graphs and that our one-mode collaboration network model captures the crucial mechanisms of the three real systems. Our study reveals that size growth, individual aging, random collaboration, preferential collaboration, transitivity collaboration and multi-round collaboration are the crucial mechanisms of collaboration networks, and the lack of some of the crucial mechanisms is the main reason that the other available models do not perform as well as ours.

Original languageEnglish (US)
Pages (from-to)270-279
Number of pages10
JournalPhysica A: Statistical Mechanics and its Applications
StatePublished - Sep 1 2016


  • Bipartite networks
  • Collaboration network model
  • Collaboration networks
  • One-mode projection

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics


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