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An Empirical Study of the Relation between Community Structure and Transitivity

  • Keziban Orman
  • Vincent Labatut
  • Hocine Cherifi
Part of the Studies in Computational Intelligence book series (SCI, volume 424)

Abstract

One of the most prominent properties in real-world networks is the presence of a community structure, i.e. dense and loosely interconnected groups of nodes called communities. In an attempt to better understand this concept, we study the relationship between the strength of the community structure and the network transitivity (or clustering coefficient). Although intuitively appealing, this analysis was not performed before. We adopt an approach based on random models to empirically study how one property varies depending on the other. It turns out the transitivity increases with the community structure strength, and is also affected by the distribution of the community sizes. Furthermore, increasing the transitivity also results in a stronger community structure. More surprisingly, if a very weak community structure causes almost zero transitivity, the opposite is not true and a network with a close to zero transitivity can still have a clearly defined community structure. Further analytical work is necessary to characterize the exact nature of the identified relationship.

Keywords

Community Structure Degree Distribution Community Detection Transitive Network Community Detection Algorithm 
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 2013

Authors and Affiliations

  • Keziban Orman
    • 1
    • 2
  • Vincent Labatut
    • 1
  • Hocine Cherifi
    • 2
  1. 1.Galatasaray UniversityIstanbulTurkey
  2. 2.University of BurgundyDijonFrance

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