Random intersection graphs with communities

Remco van der Hofstad; Júlia Komjáthy; Viktória Vadon

doi:10.1017/apr.2021.12

Random intersection graphs with communities

Remco van der Hofstad, Júlia Komjáthy, Viktória Vadon

Onderzoeksoutput: Bijdrage aan tijdschrift › Tijdschriftartikel › Academic › peer review

6 Citaten (Scopus)

Samenvatting

Random intersection graphs model networks with communities, assuming an underlying bipartite structure of communities and individuals, where these communities may overlap. We generalize the model, allowing for arbitrary community structures within the communities. In our new model, communities may overlap, and they have their own internal structure described by arbitrary finite community graphs. Our model turns out to be tractable. We analyze the overlapping structure of the communities, show local weak convergence (including convergence of subgraph counts), and derive the asymptotic degree distribution and the local clustering coefficient.

Originele taal-2	Engels
Pagina's (van-tot)	1061-1089
Aantal pagina's	29
Tijdschrift	Advances in Applied Probability
Volume	53
Nummer van het tijdschrift	4
DOI's	https://doi.org/10.1017/apr.2021.12
Status	Gepubliceerd - 12 dec. 2021

Bibliografische nota

Publisher Copyright:
© The Author(s) 2021. Published by Cambridge University Press on behalf of Applied Probability Trust.

Financiering

This work is supported by the Netherlands Organisation for Scientific Research (NWO) through the VICI grant 639.033.806 (R. v. d. H.), the VENI grant 639.031.447 (J. K.), the Gravitation Networks grant 024.002.003 (R. v. d. H.), and the TOP grant 613.001.451 (V. V.). V. V. thanks Lorenzo Federico and Clara Stegehuis for helpful discussions throughout the project.

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title = "Random intersection graphs with communities",

abstract = "Random intersection graphs model networks with communities, assuming an underlying bipartite structure of communities and individuals, where these communities may overlap. We generalize the model, allowing for arbitrary community structures within the communities. In our new model, communities may overlap, and they have their own internal structure described by arbitrary finite community graphs. Our model turns out to be tractable. We analyze the overlapping structure of the communities, show local weak convergence (including convergence of subgraph counts), and derive the asymptotic degree distribution and the local clustering coefficient. ",

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N2 - Random intersection graphs model networks with communities, assuming an underlying bipartite structure of communities and individuals, where these communities may overlap. We generalize the model, allowing for arbitrary community structures within the communities. In our new model, communities may overlap, and they have their own internal structure described by arbitrary finite community graphs. Our model turns out to be tractable. We analyze the overlapping structure of the communities, show local weak convergence (including convergence of subgraph counts), and derive the asymptotic degree distribution and the local clustering coefficient.

AB - Random intersection graphs model networks with communities, assuming an underlying bipartite structure of communities and individuals, where these communities may overlap. We generalize the model, allowing for arbitrary community structures within the communities. In our new model, communities may overlap, and they have their own internal structure described by arbitrary finite community graphs. Our model turns out to be tractable. We analyze the overlapping structure of the communities, show local weak convergence (including convergence of subgraph counts), and derive the asymptotic degree distribution and the local clustering coefficient.

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Random intersection graphs with communities

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