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Network Motifs Are a Powerful Tool for Semantic Distinction

  • Chris BiemannEmail author
  • Lachezar Krumov
  • Stefanie Roos
  • Karsten Weihe
Part of the Understanding Complex Systems book series (UCS)

Abstract

Motifs are a general network analysis technique, which statistically relates network structure to epiphenomena on the network. This technique has been developed and brought to maturity in molecular biology, where it has been successfully applied to network-based chemical and biological dynamics of various types. Early on, the motif technique has been successfully applied outside biology as well – to social networks, electrical networks, and many more. Results by Milo et al. showed that the motif signature of a network varies from realm to realm to some extent but is significantly more homogenous within a realm. This observation has been the starting point of the thread of research presented in this paper. More specifically, we do not compare networks from different realms but focus on networks from a given realm. In several case studies on particular realms, we found that motif signatures suffice to distinguish certain classes of networks from each other. In this paper, we summarize our previous work, and present some new results. In particular, in Biemann et al. (2012), we found that natural and artificially generated language can be distinguished from each other through the motif signatures of the co-occurrence graphs. Based on that, we present work on co-occurrence graphs that are restricted to word classes. We found that the co-occurrence graphs of verbs (and other word classes used like predicates) exhibit strongly different motif signatures and can be distinguished by that. To demonstrate the general power of the approach, we present further original work on co-authorship networks, peer-to-peer streaming networks, and mailing networks.

Keywords

Motif Signature Network Motif Motif Analysis Word Class Natural Language Semantic 
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 2016

Authors and Affiliations

  • Chris Biemann
    • 1
    Email author
  • Lachezar Krumov
    • 1
  • Stefanie Roos
    • 1
  • Karsten Weihe
    • 1
  1. 1.Computer Science DepartmentTechnische Universität DarmstadtDarmstadtGermany

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