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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- 1.
By subgraph we mean any subgraph, not just the induced ones.
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Karjalainen, J., van Leeuwaarden, J.S.H., Leskelä, L. (2018). Parameter Estimators of Sparse Random Intersection Graphs with Thinned Communities. In: Bonato, A., Prałat, P., Raigorodskii, A. (eds) Algorithms and Models for the Web Graph. WAW 2018. Lecture Notes in Computer Science(), vol 10836. Springer, Cham. https://doi.org/10.1007/978-3-319-92871-5_4
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