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Data Science, Learning by Latent Structures, and Knowledge Discovery pp 79–88Cite as

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

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Abstract

Experimental results show that in large complex networks (such as internet, social or biological networks) there exists a tendency to connect elements which have a common neighbor. In theoretical random graph models, this tendency is described by the clustering coefficient being bounded away from zero. Complex networks also have power-law degree distributions and short average distances (small world phenomena). These are desirable features of random graphs used for modeling real life networks. We survey recent results concerning various random intersection graph models showing that they have tunable clustering coefficient, a rich class of degree distributions including power-laws, and short average distances.

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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 acknowledge the support by the National Science Centre (NCN)—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).

Author information

Authors and Affiliations

  1. Faculty of Mathematics and Informatics, Vilnius University, 03225, Vilnius, Lithuania

    Mindaugas Bloznelis & Valentas Kurauskas

  2. Clinic of Cardiovascular Surgery, Heinrich Heine University, 40225, Düsseldorf, Germany

    Erhard Godehardt

  3. Faculty of Mathematics and Computer Science, Adam Mickiewicz University, 60769, Poznań, Poland

    Jerzy Jaworski & Katarzyna Rybarczyk

Authors
  1. Mindaugas Bloznelis
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  2. Erhard Godehardt
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  3. Jerzy Jaworski
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  4. Valentas Kurauskas
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  5. Katarzyna Rybarczyk
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Corresponding author

Correspondence to Erhard Godehardt .

Editor information

Editors and Affiliations

  1. University of Essex, Colchester, United Kingdom

    Berthold Lausen

  2. University of Luxembourg, Walferdange, Luxembourg

    Sabine Krolak-Schwerdt

  3. University of Luxembourg, Walferdange, Luxembourg

    Matthias Böhmer

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Bloznelis, M., Godehardt, E., Jaworski, J., Kurauskas, V., Rybarczyk, K. (2015). Recent Progress in Complex Network Analysis: Properties of Random Intersection Graphs. In: Lausen, B., Krolak-Schwerdt, S., Böhmer, M. (eds) Data Science, Learning by Latent Structures, and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44983-7_7

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