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Recent Progress in Complex Network Analysis: Models of Random Intersection Graphs

  • Mindaugas Bloznelis
  • Erhard GodehardtEmail author
  • Jerzy Jaworski
  • Valentas Kurauskas
  • Katarzyna Rybarczyk
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Experimental results show that in large complex networks such as Internet or biological networks, there is a tendency to connect elements which have a common neighbor. This tendency in theoretical random graph models is depicted by the asymptotically constant clustering coefficient. Moreover complex networks have power law degree distribution and small diameter (small world phenomena), thus these are desirable features of random graphs used for modeling real life networks. We survey various variants of random intersection graph models, which are important for networks modeling.

Notes

Acknowledgements

The work of M. Bloznelis and V. Kurauskas was supported by the Lithuanian Research Council (grant MIP–067/2013). J. Jaworski and K. Rybarczyk were supported by the National Science Centre—DEC-2011/01/B/ST1/03943. Co-operation between E. Godehardt and J. Jaworski was also supported by Deutsche Forschungsgemeinschaft (grant no. GO 490/17–1).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Mindaugas Bloznelis
    • 1
  • Erhard Godehardt
    • 2
    Email author
  • Jerzy Jaworski
    • 3
  • Valentas Kurauskas
    • 1
  • Katarzyna Rybarczyk
    • 3
  1. 1.Faculty of Mathematics and InformaticsVilnius UniversityVilniusLithuania
  2. 2.Clinic of Cardiovascular SurgeryHeinrich Heine UniversityDüsseldorfGermany
  3. 3.Faculty of Mathematics and Computer ScienceAdam Mickiewicz UniversityPoznańPoland

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