Finding biologically plausible complex network topologies with a new evolutionary approach for network generation

Gordon Govan; Jakub Chlanda; David Corne; Alexandros O Xenos; Pierluigi Frisco

doi:10.1007/978-3-319-01128-8_5

Finding biologically plausible complex network topologies with a new evolutionary approach for network generation

Gordon Govan^*, Jakub Chlanda, David Corne, Alexandros O Xenos, Pierluigi Frisco

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

We explore the recently introduced Structured Nodes (SN) network model, for which earlier work has shown its capability in matching several topological properties of complex networks. We consider a diverse set of empirical biological complex networks as targets and we use an evolutionary algorithm (EA) approach to identify input for the SN model allowing it to generate networks similar to these targets. We find that by using the EA the structural fit between SN networks and the targets is improved. The combined SN/EA approach is a promising direction to further investigate the growth, properties and behaviour of biological networks.

Original language	English
Title of host publication	Advances in Intelligent Systems and Computing
Pages	59-73
Number of pages	15
Volume	227
DOIs	https://doi.org/10.1007/978-3-319-01128-8_5
Publication status	Published - 29 Aug 2013
Event	International Conference on EVOLVE - Leiden, United Kingdom Duration: 10 Jul 2013 → 13 Jul 2013

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	227
ISSN (Print)	21945357

Conference

Conference	International Conference on EVOLVE
Country/Territory	United Kingdom
City	Leiden
Period	10/07/13 → 13/07/13

ASJC Scopus subject areas

General Computer Science
Control and Systems Engineering

Access to Document

10.1007/978-3-319-01128-8_5

Cite this

@inproceedings{a68df6912a1e44a29dc808068882b1cd,

title = "Finding biologically plausible complex network topologies with a new evolutionary approach for network generation",

abstract = "We explore the recently introduced Structured Nodes (SN) network model, for which earlier work has shown its capability in matching several topological properties of complex networks. We consider a diverse set of empirical biological complex networks as targets and we use an evolutionary algorithm (EA) approach to identify input for the SN model allowing it to generate networks similar to these targets. We find that by using the EA the structural fit between SN networks and the targets is improved. The combined SN/EA approach is a promising direction to further investigate the growth, properties and behaviour of biological networks.",

author = "Gordon Govan and Jakub Chlanda and David Corne and Xenos, {Alexandros O} and Pierluigi Frisco",

year = "2013",

month = aug,

day = "29",

doi = "10.1007/978-3-319-01128-8_5",

language = "English",

isbn = "9783319011271",

volume = "227",

series = "Advances in Intelligent Systems and Computing",

pages = "59--73",

booktitle = "Advances in Intelligent Systems and Computing",

note = "International Conference on EVOLVE ; Conference date: 10-07-2013 Through 13-07-2013",

}

Govan, G, Chlanda, J, Corne, D, Xenos, AO & Frisco, P 2013, Finding biologically plausible complex network topologies with a new evolutionary approach for network generation. in Advances in Intelligent Systems and Computing. vol. 227, Advances in Intelligent Systems and Computing, vol. 227, pp. 59-73, International Conference on EVOLVE, Leiden, United Kingdom, 10/07/13. https://doi.org/10.1007/978-3-319-01128-8_5

Finding biologically plausible complex network topologies with a new evolutionary approach for network generation. / Govan, Gordon; Chlanda, Jakub; Corne, David et al.
Advances in Intelligent Systems and Computing. Vol. 227 2013. p. 59-73 (Advances in Intelligent Systems and Computing; Vol. 227).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Finding biologically plausible complex network topologies with a new evolutionary approach for network generation

AU - Govan, Gordon

AU - Chlanda, Jakub

AU - Corne, David

AU - Xenos, Alexandros O

AU - Frisco, Pierluigi

PY - 2013/8/29

Y1 - 2013/8/29

N2 - We explore the recently introduced Structured Nodes (SN) network model, for which earlier work has shown its capability in matching several topological properties of complex networks. We consider a diverse set of empirical biological complex networks as targets and we use an evolutionary algorithm (EA) approach to identify input for the SN model allowing it to generate networks similar to these targets. We find that by using the EA the structural fit between SN networks and the targets is improved. The combined SN/EA approach is a promising direction to further investigate the growth, properties and behaviour of biological networks.

AB - We explore the recently introduced Structured Nodes (SN) network model, for which earlier work has shown its capability in matching several topological properties of complex networks. We consider a diverse set of empirical biological complex networks as targets and we use an evolutionary algorithm (EA) approach to identify input for the SN model allowing it to generate networks similar to these targets. We find that by using the EA the structural fit between SN networks and the targets is improved. The combined SN/EA approach is a promising direction to further investigate the growth, properties and behaviour of biological networks.

U2 - 10.1007/978-3-319-01128-8_5

DO - 10.1007/978-3-319-01128-8_5

M3 - Conference contribution

AN - SCOPUS:84882994160

SN - 9783319011271

VL - 227

T3 - Advances in Intelligent Systems and Computing

SP - 59

EP - 73

BT - Advances in Intelligent Systems and Computing

T2 - International Conference on EVOLVE

Y2 - 10 July 2013 through 13 July 2013

ER -

Finding biologically plausible complex network topologies with a new evolutionary approach for network generation

Abstract

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