Randomization algorithms for large sparse networks

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • Finnish Institute of Occupational Health

Abstract

In many domains it is necessary to generate surrogate networks, e.g., for hypothesis testing of different properties of a network. Generating surrogate networks typically requires that different properties of the network are preserved, e.g., edges may not be added or deleted and edge weights may be restricted to certain intervals. In this paper we present an efficient property-preserving Markov chain Monte Carlo method termed CycleSampler for generating surrogate networks in which (1) edge weights are constrained to intervals and vertex strengths are preserved exactly, and (2) edge and vertex strengths are both constrained to intervals. These two types of constraints cover a wide variety of practical use cases. The method is applicable to both undirected and directed graphs. We empirically demonstrate the efficiency of the CycleSampler method on real-world data sets. We provide an implementation of CycleSampler in R, with parts implemented in C.

Details

Original languageEnglish
Article number053311
Pages (from-to)1-15
Number of pages15
JournalPhysical Review E
Volume99
Issue number5
Publication statusPublished - 30 May 2019
MoE publication typeA1 Journal article-refereed

ID: 35132749