Growing scale-free networks with tunable distributions of triad motifs
Author
dc.contributor.author
Li, Shuguang
Author
dc.contributor.author
Yuanb, Jianping
Author
dc.contributor.author
Shi, Yong
Author
dc.contributor.author
Zagal Montealegre, Juan
Admission date
dc.date.accessioned
2015-07-09T18:38:20Z
Available date
dc.date.available
2015-07-09T18:38:20Z
Publication date
dc.date.issued
2015
Cita de ítem
dc.identifier.citation
Physica A 428 (2015) 103–110
en_US
Identifier
dc.identifier.issn
0378-4371
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/131892
General note
dc.description
Artículo de publicación ISI
en_US
Abstract
dc.description.abstract
Network motifs are local structural patterns and elementary functional units of complex
networks in real world, which can have significant impacts on the global behavior of these
systems. Many models are able to reproduce complex networks mimicking a series of global
features of real systems, however the local features such as motifs in real networks have not
been well represented.Wepropose a model to grow scale-free networks with tunable motif
distributions through a combined operation of preferential attachment and triad motif
seeding steps. Numerical experiments show that the constructed networks have adjustable
distributions of the local triad motifs, meanwhile preserving the global features of powerlaw
distributions of node degree, short average path lengths of nodes, and highly clustered
structures.