Parameter Estimators of Sparse Random Intersection Graphs with Thinned Communities

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Research units

  • Eindhoven University of Technology


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.


Original languageEnglish
Title of host publicationAlgorithms and Models for the Web Graph
Subtitle of host publication15th International Workshop, WAW 2018, Moscow, Russia, May 17-18, 2018, Proceedings
EditorsAnthony Bonato, Paweł Prałat, Andrei Raigorodskii
Publication statusPublished - May 2018
MoE publication typeA4 Article in a conference publication
EventInternational Workshop on Algorithms and Models for the Web Graph - Moscow, Russian Federation
Duration: 17 May 201818 May 2018
Conference number: 15

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


WorkshopInternational Workshop on Algorithms and Models for the Web Graph
Abbreviated titleWWW
CountryRussian Federation

    Research areas

  • Random Graphs, Statistics, Network Analysis, Probability

ID: 21779775