Entangled networks, synchronization, and optimal network topology.

By L. Donetti, P. Hurtado, Miguel Angel Muñoz. Physical Review Letters, 2005

317 citations recorded by the source corpus.

A new family of graphs, entangled networks, with optimal properties in many respects, is introduced. By definition, their topology is such that it optimizes synchronizability for many dynamical processes. These networks are shown to have an extremely homogeneous structure: degree, node distance, betweenness, and loop distributions are all very narrow. Also, they are characterized by a very interwoven (entangled) structure with short average distances, large loops, and no well-defined community structure. This family of nets exhibits an excellent performance with respect to other flow properties such as robustness against errors and attacks, minimal first-passage time of random walks, efficient communication, etc. These remarkable features convert entangled networks in a useful concept, optimal or almost optimal in many senses, and with plenty of potential applications in computer science or neuroscience.

Subfields: Computational Theory and Mathematics, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d00566b37702da8317071bb84f035166ee17525e

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Citations in the corpus

The application of graph theoretical analysis to complex networks in the brain. (2007, unknown): “The application of the ratio R as a measure of synchronizability has demonstrated that factors such as heterogeneity of the degree distribution and average degree have different effects on synchronizability of weighted and unweighted networks (Nishikawa et al., 2003; Donetti et al., 2005; Motter et ”
Distributed Consensus With Limited Communication Data Rate (2011, background): “Expander graphs are highly connected sparse graphs, which play an important role in designing efficient communication networks in computer science ([30], [40]).”
Synchronization in complex networks (2008, methodology): “Donetti et al. (2005) ap- plied an optimization scheme (e.g. simulated annealing) combined with a network rewiring algorithm to minimize the eigenratio R.”
Graph theoretical analysis of complex networks in the brain (2007, methodology): “Donetti et al. (2005) described an algorithm to generate such 'hyper homogeneous' networks which they baptized 'entangled networks' [ 56 ].”