Weak Ties in Complex Wireless Communication Networks

  • Amanda Leonel
  • Carlos H. C. Ribeiro
  • Matthias R. Brust
Part of the Studies in Computational Intelligence book series (SCI, volume 424)


Hundreds of millions of devices—from book-sized notebooks to tiny hand-held mobile phones—are equipped with wireless communication adapters that are able to form a network among themselves. The spontaneous creation of this kind of network and the unpredictable joining and leaving of devices bring forward new challenges on network and topology organization. Network Science has proven to deliver a fruitful methodology to investigate systems such as complex communication networks, and new insights and solutions can be gained by understanding and imitating the function and structure of social networks. Following this line, this paper initially focuses on the development of models that reveal characteristics found to be inherent to social networks. In particular, we consider the finding that social networks can contain a diversity of links: we create clusters of friends, connected by strong links and, additionally, there are links to acquaintances, the so-called weak ties which, despite the name, have been hypothesized as essential for finding jobs or disseminating rumors when strong ties fail. As such links seem to be highly important to deal with the requirements of a complex network such as our own social network, we argue that bringing these structures to the design principles of complex communication networks may result in an increase of efficiency and robustness, and we describe the implementation of two algorithms for wireless communication networks using only local neighborhood information and producing features of complex social networks (weak ties in particular). The results imply that local removing promotes the emergence of weak ties, which we found by using a recently proposed link clustering algorithm for identifying link communities.


Link Community Spatial Network Unit Disk Graph Average Short Path Link Similarity 
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

  • Amanda Leonel
    • 1
  • Carlos H. C. Ribeiro
    • 1
  • Matthias R. Brust
    • 2
  1. 1.Computer Science DivisionTechnological Institute of AeronauticsSão José dos CamposBrazil
  2. 2.Department of Electrical Engineering and Computer ScienceUniversity of Central FloridaOrlandoUSA

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