Fast generation of sparse random kernel graphs
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Beihang Univ. (China)
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.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1222471
- Journal Information:
- PLoS ONE, Journal Name: PLoS ONE Journal Issue: 9 Vol. 10; ISSN 1932-6203
- Publisher:
- Public Library of ScienceCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Constructing and sampling graphs with a given joint degree distribution.
Coloring geographical threshold graphs
Evolution of a Modified Binomial Random Graph by Agglomeration
Conference
·
Wed Sep 01 00:00:00 EDT 2010
·
OSTI ID:1030342
Coloring geographical threshold graphs
Conference
·
Mon Dec 31 23:00:00 EST 2007
·
OSTI ID:956667
Evolution of a Modified Binomial Random Graph by Agglomeration
Journal Article
·
Wed Feb 14 23:00:00 EST 2018
· Journal of Statistical Physics
·
OSTI ID:22784013