Motif Finding Algorithms: A Performance Comparison

Emanuele Martorana, Roberto Grasso, Giovanni Micale, Salvatore Alaimo, Dennis Shasha, Rosalba Giugno, Alfredo Pulvirenti

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Network motifs are subgraphs of a network that occur more frequently than expected, according to some reasonable null model. They represent building blocks of complex systems such as genetic interaction networks or social networks and may reveal intriguing typical but perhaps unexpected relationships between interacting entities. The identification of network motif is a time consuming task since it subsumes the subgraph matching problem. Several algorithms have been proposed in the literature. In this paper we aim to review the motif finding problem through a systematic comparison of state-of-the-art algorithms on both real and artificial networks of different sizes. We aim to provide readers a complete overview of the performance of the various tools. As far as we know, this is the most comprehensive experimental review of motif finding algorithms to date, with respect both to the number of compared tools and to the variety and size of networks used for the experiments.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Science and Business Media Deutschland GmbH
Pages250-267
Number of pages18
DOIs
StatePublished - 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14070 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Network motifs
  • Network motifs search
  • Network motifs significance
  • Network motifs tools comparison

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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