Fast generation of sparse random kernel graphs
Abstract
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Beihang Univ. (China)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1222471
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- PLoS ONE
- Additional Journal Information:
- Journal Volume: 10; Journal Issue: 9; Journal ID: ISSN 1932-6203
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; graphs; algorithms; kernel methods; random variables; integrals; computing systems; mathematical models; network analysis
Citation Formats
Hagberg, Aric, Lemons, Nathan, and Du, Wen -Bo. Fast generation of sparse random kernel graphs. United States: N. p., 2015.
Web. doi:10.1371/journal.pone.0135177.
Hagberg, Aric, Lemons, Nathan, & Du, Wen -Bo. Fast generation of sparse random kernel graphs. United States. https://doi.org/10.1371/journal.pone.0135177
Hagberg, Aric, Lemons, Nathan, and Du, Wen -Bo. 2015.
"Fast generation of sparse random kernel graphs". United States. https://doi.org/10.1371/journal.pone.0135177. https://www.osti.gov/servlets/purl/1222471.
@article{osti_1222471,
title = {Fast generation of sparse random kernel graphs},
author = {Hagberg, Aric and Lemons, Nathan and Du, Wen -Bo},
abstractNote = {The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.},
doi = {10.1371/journal.pone.0135177},
url = {https://www.osti.gov/biblio/1222471},
journal = {PLoS ONE},
issn = {1932-6203},
number = 9,
volume = 10,
place = {United States},
year = {Thu Sep 10 00:00:00 EDT 2015},
month = {Thu Sep 10 00:00:00 EDT 2015}
}
Web of Science
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