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Link Prediction Using Top-k Shortest Distances

  • Andrei Lebedev
  • JooYoung Lee
  • Victor RiveraEmail author
  • Manuel Mazzara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10365)

Abstract

Top-k shortest path routing problem is an extension of finding the shortest path in a given network. Shortest path is one of the most essential measures as it reveals the relations between two nodes in a network. However, in many real world networks, whose diameters are small, top-k shortest path is more interesting as it contains more information about the network topology. In this paper, we apply an efficient top-k shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Our results show that using top-k distances as a similarity measure outperforms classical similarity measures such as Jaccard and Adamic/Adar.

Keywords

Graph databases Shortest paths Link prediction Graph matching Similarity 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andrei Lebedev
    • 1
  • JooYoung Lee
    • 1
  • Victor Rivera
    • 1
    Email author
  • Manuel Mazzara
    • 1
  1. 1.Innnopolis UniversityInnopolisRussia

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