Journal article

Identifying missing and spurious connections via the bi-directional diffusion on bipartite networks

  • Zhang, Peng School of Science, Beijing University of Posts and Telecommunications, Beijing, China
  • Zeng, An Department of Physics, University of Fribourg, Switzerland - School of Systems Science, Beijing Normal University, China
  • Fan, Ying School of Systems Science, Beijing Normal University, China
Published in:
  • Physics Letters A. - 2014, vol. 378, no. 32–33, p. 2350–2354
English Link prediction and spurious link detection in complex networks have attracted increasing attention from both physical and computer science communities, due to their wide applications in many real systems. Related previous works mainly focus on monopartite networks while these problems in bipartite networks are not yet systematically addressed. Containing two different kinds of nodes, bipartite networks are essentially different from monopartite networks, especially in node similarity calculation: the similarity between nodes of different kinds (called inter-similarity) is not well defined. In this letter, we employ the local diffusion processes to measure the inter-similarity in bipartite networks. We find that the inter-similarity is asymmetric if the diffusion is applied in different directions. Accordingly, we propose a bi-directional hybrid diffusion method which is shown to achieve higher accuracy than the existing diffusion methods in identifying missing and spurious links in bipartite networks.
Faculté des sciences et de médecine
Département de Physique
  • English
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