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Title: Fast generation of sparse random kernel graphs

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:
 [1] ;  [1] ;  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Beihang Univ. (China)
Publication Date:
OSTI Identifier:
1222471
Grant/Contract Number:
AC52-06NA25396
Type:
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
Research Org:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
graphs; algorithms; kernel methods; random variables; integrals; computing systems; mathematical models; network analysis