arXiv Analytics

Sign in

arXiv:cond-mat/0409095AbstractReferencesReviewsResources

Geographical Coarsegraining of Complex Networks

Beom Jun Kim

Published 2004-09-04Version 1

We perform the renormalization-group-like numerical analysis of geographically embedded complex networks on the two-dimensional square lattice. At each step of coarsegraining procedure, the four vertices on each $2 \times 2$ square box are merged to a single vertex, resulting in the coarsegrained system of the smaller sizes. Repetition of the process leads to the observation that the coarsegraining procedure does not alter the qualitative characteristics of the original scale-free network, which opens the possibility of subtracting a smaller network from the original network without destroying the important structural properties. The implication of the result is also suggested in the context of the recent study of the human brain functional network.

Related articles: Most relevant | Search more
arXiv:1402.4426 [cond-mat.dis-nn] (Published 2014-02-18)
Fluctuations analysis in complex networks modeled by hidden variable models. Necessity of a large cut-off in hidden-variable models
arXiv:0907.1514 [cond-mat.dis-nn] (Published 2009-07-09, updated 2009-10-16)
Entropy measures for complex networks: Toward an information theory of complex topologies
arXiv:1012.0203 [cond-mat.dis-nn] (Published 2010-12-01)
Enhancing synchronization by directionality in complex networks