The Link Prediction Problem in Bipartite Networks

  • Jérôme Kunegis
  • Ernesto W. De Luca
  • Sahin Albayrak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6178)


We define and study the link prediction problem in bipartite networks, specializing general link prediction algorithms to the bipartite case. In a graph, a link prediction function of two vertices denotes the similarity or proximity of the vertices. Common link prediction functions for general graphs are defined using paths of length two between two nodes. Since in a bipartite graph adjacency vertices can only be connected by paths of odd lengths, these functions do not apply to bipartite graphs. Instead, a certain class of graph kernels (spectral transformation kernels) can be generalized to bipartite graphs when the positive-semidefinite kernel constraint is relaxed. This generalization is realized by the odd component of the underlying spectral transformation. This construction leads to several new link prediction pseudokernels such as the matrix hyperbolic sine, which we examine for rating graphs, authorship graphs, folksonomies, document–feature networks and other types of bipartite networks.


Bipartite Graph Preferential Attachment Mean Average Precision Link Prediction Bipartite Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jérôme Kunegis
    • 1
  • Ernesto W. De Luca
    • 1
  • Sahin Albayrak
    • 1
  1. 1.DAI LabTechnische Universität BerlinBerlinGermany

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