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Multiple Global Community Detection in Signed Graphs

  • Ehsan Zahedinejad
  • Daniel Crawford
  • Clemens Adolphs
  • Jaspreet S. OberoiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)

Abstract

Signed graphs serve as a primary tool for modelling social networks. They can represent relationships between individuals (i.e., nodes) with the use of signed edges. Finding communities in a signed graph is of great importance in many areas, for example, targeted advertisement. We propose an algorithm to detect multiple communities in a signed graph. Our method reduces the multi-community detection problem to a quadratic binary unconstrained optimization problem and uses state-of-the-art quantum or classical optimizers to find an optimal assignment of each individual to a specific community.

Keywords

Graph clustering Modularity Frustration Community detection Quantum annealing Discrete optimization 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ehsan Zahedinejad
    • 1
  • Daniel Crawford
    • 1
  • Clemens Adolphs
    • 1
  • Jaspreet S. Oberoi
    • 1
    • 2
    Email author
  1. 1.1QB Information TechnologiesVancouverCanada
  2. 2.School of Engineering ScienceSimon Fraser UniversityBurnabyCanada

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