Homogeneous complex networks

L  Bogacz; Z  Burda; B  Waclaw

doi:10.1016/j.physa.2005.10.024

Homogeneous complex networks

L Bogacz, Z Burda, B Waclaw

School of Physics and Astronomy

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

We discuss various ensembles of homogeneous complex networks and a Monte-Carlo method of generating graphs from these ensembles. The method is quite general and can be applied to simulate micro-canonical, canonical or grand-canonical ensembles for systems with various statistical weights. It can be used to construct homogeneous networks with desired properties, or to construct a non-trivial scoring function for problems of advanced motif searching. (c) 2005 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	587-607
Number of pages	21
Journal	Physica a-Statistical mechanics and its applications
Volume	366
Issue number	1
DOIs	https://doi.org/10.1016/j.physa.2005.10.024
Publication status	Published - 1 Jul 2006

Keywords / Materials (for Non-textual outputs)

complex networks
random graphs
statistical mechanics
Markov process
STATISTICAL-MECHANICS
RANDOM GRAPHS
DEGREE SEQUENCE
EVOLUTION
SIZE

Access to Document

10.1016/j.physa.2005.10.024

Cite this

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title = "Homogeneous complex networks",

abstract = "We discuss various ensembles of homogeneous complex networks and a Monte-Carlo method of generating graphs from these ensembles. The method is quite general and can be applied to simulate micro-canonical, canonical or grand-canonical ensembles for systems with various statistical weights. It can be used to construct homogeneous networks with desired properties, or to construct a non-trivial scoring function for problems of advanced motif searching. (c) 2005 Elsevier B.V. All rights reserved.",

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AU - Waclaw, B

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N2 - We discuss various ensembles of homogeneous complex networks and a Monte-Carlo method of generating graphs from these ensembles. The method is quite general and can be applied to simulate micro-canonical, canonical or grand-canonical ensembles for systems with various statistical weights. It can be used to construct homogeneous networks with desired properties, or to construct a non-trivial scoring function for problems of advanced motif searching. (c) 2005 Elsevier B.V. All rights reserved.

AB - We discuss various ensembles of homogeneous complex networks and a Monte-Carlo method of generating graphs from these ensembles. The method is quite general and can be applied to simulate micro-canonical, canonical or grand-canonical ensembles for systems with various statistical weights. It can be used to construct homogeneous networks with desired properties, or to construct a non-trivial scoring function for problems of advanced motif searching. (c) 2005 Elsevier B.V. All rights reserved.

KW - complex networks

KW - random graphs

KW - statistical mechanics

KW - Markov process

KW - STATISTICAL-MECHANICS

KW - RANDOM GRAPHS

KW - DEGREE SEQUENCE

KW - EVOLUTION

KW - SIZE

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DO - 10.1016/j.physa.2005.10.024

M3 - Article

SN - 0378-4371

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EP - 607

JO - Physica a-Statistical mechanics and its applications

JF - Physica a-Statistical mechanics and its applications

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