Network motifs detection using random networks with prescribed subgraph frequencies

Miguel E.P. Silva, Pedro Paredes, Pedro Ribeiro

Research output: Chapter in Book/Report/Conference proceedingChapter

8 Scopus citations

Abstract

In order to detect network motifs we need to evaluate the exceptionality of subgraphs in a given network. This is usually done by comparing subgraph frequencies on both the original and an ensemble of random networks keeping certain structural properties. The classical null model implies preserving the degree sequence. In this paper our focus is on a richer model that approximately fixes the frequency of subgraphs of size K - 1 to compute motifs of size K. We propose a method for generating random graphs under this model, and we provide algorithms for its efficient computation. We show empirical results of our proposed methodology on neurobiological networks, showcasing its efficiency and its differences when comparing to the traditional null model.

Original languageEnglish (US)
Title of host publicationSpringer Proceedings in Complexity
PublisherSpringer
Pages17-29
Number of pages13
DOIs
StatePublished - 2017
Externally publishedYes

Publication series

NameSpringer Proceedings in Complexity
ISSN (Print)2213-8684
ISSN (Electronic)2213-8692

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Modeling and Simulation
  • Computer Science Applications

Keywords

  • Network motifs
  • Random graphs
  • Subgraph counting

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