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Parameter Estimators of Sparse Random Intersection Graphs with Thinned Communities

  • Joona KarjalainenEmail author
  • Johan S. H. van Leeuwaarden
  • Lasse Leskelä
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10836)

Abstract

This paper studies a statistical network model generated by a large number of randomly sized overlapping communities, where any pair of nodes sharing a community is linked with probability q via the community. In the special case with \(q=1\) the model reduces to a random intersection graph which is known to generate high levels of transitivity also in the sparse context. The parameter q adds a degree of freedom and leads to a parsimonious and analytically tractable network model with tunable density, transitivity, and degree fluctuations. We prove that the parameters of this model can be consistently estimated in the large and sparse limiting regime using moment estimators based on partially observed densities of links, 2-stars, and triangles.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Joona Karjalainen
    • 1
    Email author
  • Johan S. H. van Leeuwaarden
    • 2
  • Lasse Leskelä
    • 1
  1. 1.Aalto UniversityEspooFinland
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands

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