SuperNoder

a tool to discover over-represented modular structures in networks

Danilo Dessì, Jacopo Cirrone, Diego Reforgiato Recupero, Dennis Shasha

Research output: Contribution to journalArticle

Abstract

BACKGROUND: Networks whose nodes have labels can seem complex. Fortunately, many have substructures that occur often ("motifs"). A societal example of a motif might be a household. Replacing such motifs by named supernodes reduces the complexity of the network and can bring out insightful features. Doing so repeatedly may give hints about higher level structures of the network. We call this recursive process Recursive Supernode Extraction.

RESULTS: This paper describes algorithms and a tool to discover disjoint (i.e. non-overlapping) motifs in a network, replacing those motifs by new nodes, and then recursing. We show applications in food-web and protein-protein interaction (PPI) networks where our methods reduce the complexity of the network and yield insights.

CONCLUSIONS: SuperNoder is a web-based and standalone tool which enables the simplification of big graphs based on the reduction of high frequency motifs. It applies various strategies for identifying disjoint motifs with the goal of enhancing the understandability of networks.

Original languageEnglish (US)
Number of pages1
JournalBMC Bioinformatics
Volume19
Issue number1
DOIs
StatePublished - Sep 10 2018

Fingerprint

Protein Interaction Maps
Food Chain
Proteins
Labels
Disjoint
Food Web
Protein Interaction Networks
Protein-protein Interaction
Substructure
Vertex of a graph
Web-based
Simplification
Graph in graph theory

Keywords

  • Computational complexity
  • Food-web network
  • Motifs discovery
  • Network compression
  • PPI interaction network

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

SuperNoder : a tool to discover over-represented modular structures in networks. / Dessì, Danilo; Cirrone, Jacopo; Recupero, Diego Reforgiato; Shasha, Dennis.

In: BMC Bioinformatics, Vol. 19, No. 1, 10.09.2018.

Research output: Contribution to journalArticle

Dessì, Danilo ; Cirrone, Jacopo ; Recupero, Diego Reforgiato ; Shasha, Dennis. / SuperNoder : a tool to discover over-represented modular structures in networks. In: BMC Bioinformatics. 2018 ; Vol. 19, No. 1.
@article{8ff0675636364847a846a5112f87d315,
title = "SuperNoder: a tool to discover over-represented modular structures in networks",
abstract = "BACKGROUND: Networks whose nodes have labels can seem complex. Fortunately, many have substructures that occur often ({"}motifs{"}). A societal example of a motif might be a household. Replacing such motifs by named supernodes reduces the complexity of the network and can bring out insightful features. Doing so repeatedly may give hints about higher level structures of the network. We call this recursive process Recursive Supernode Extraction.RESULTS: This paper describes algorithms and a tool to discover disjoint (i.e. non-overlapping) motifs in a network, replacing those motifs by new nodes, and then recursing. We show applications in food-web and protein-protein interaction (PPI) networks where our methods reduce the complexity of the network and yield insights.CONCLUSIONS: SuperNoder is a web-based and standalone tool which enables the simplification of big graphs based on the reduction of high frequency motifs. It applies various strategies for identifying disjoint motifs with the goal of enhancing the understandability of networks.",
keywords = "Computational complexity, Food-web network, Motifs discovery, Network compression, PPI interaction network",
author = "Danilo Dess{\`i} and Jacopo Cirrone and Recupero, {Diego Reforgiato} and Dennis Shasha",
year = "2018",
month = "9",
day = "10",
doi = "10.1186/s12859-018-2350-8",
language = "English (US)",
volume = "19",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central",
number = "1",

}

TY - JOUR

T1 - SuperNoder

T2 - a tool to discover over-represented modular structures in networks

AU - Dessì, Danilo

AU - Cirrone, Jacopo

AU - Recupero, Diego Reforgiato

AU - Shasha, Dennis

PY - 2018/9/10

Y1 - 2018/9/10

N2 - BACKGROUND: Networks whose nodes have labels can seem complex. Fortunately, many have substructures that occur often ("motifs"). A societal example of a motif might be a household. Replacing such motifs by named supernodes reduces the complexity of the network and can bring out insightful features. Doing so repeatedly may give hints about higher level structures of the network. We call this recursive process Recursive Supernode Extraction.RESULTS: This paper describes algorithms and a tool to discover disjoint (i.e. non-overlapping) motifs in a network, replacing those motifs by new nodes, and then recursing. We show applications in food-web and protein-protein interaction (PPI) networks where our methods reduce the complexity of the network and yield insights.CONCLUSIONS: SuperNoder is a web-based and standalone tool which enables the simplification of big graphs based on the reduction of high frequency motifs. It applies various strategies for identifying disjoint motifs with the goal of enhancing the understandability of networks.

AB - BACKGROUND: Networks whose nodes have labels can seem complex. Fortunately, many have substructures that occur often ("motifs"). A societal example of a motif might be a household. Replacing such motifs by named supernodes reduces the complexity of the network and can bring out insightful features. Doing so repeatedly may give hints about higher level structures of the network. We call this recursive process Recursive Supernode Extraction.RESULTS: This paper describes algorithms and a tool to discover disjoint (i.e. non-overlapping) motifs in a network, replacing those motifs by new nodes, and then recursing. We show applications in food-web and protein-protein interaction (PPI) networks where our methods reduce the complexity of the network and yield insights.CONCLUSIONS: SuperNoder is a web-based and standalone tool which enables the simplification of big graphs based on the reduction of high frequency motifs. It applies various strategies for identifying disjoint motifs with the goal of enhancing the understandability of networks.

KW - Computational complexity

KW - Food-web network

KW - Motifs discovery

KW - Network compression

KW - PPI interaction network

UR - http://www.scopus.com/inward/record.url?scp=85054412495&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054412495&partnerID=8YFLogxK

U2 - 10.1186/s12859-018-2350-8

DO - 10.1186/s12859-018-2350-8

M3 - Article

VL - 19

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

IS - 1

ER -