Enhancing complex-network synchronization

By Adilson E. Motter, Changsong Zhou, J. Kurths. 2004

316 citations recorded by the source corpus.

Heterogeneity in the degree (connectivity) distribution has been shown to suppress synchronization in networks of symmetrically coupled oscillators with uniform coupling strength (unweighted coupling). Here we uncover a condition for enhanced synchronization in weighted networks with asymmetric coupling. We show that, in the optimum regime, synchronizability is solely determined by the average degree and does not depend on the system size and the details of the degree distribution. In scale-free networks, where the average degree may increase with heterogeneity, synchronizability is drastically enhanced and may become positively correlated with heterogeneity, while the overall cost involved in the network coupling is significantly reduced as compared to the case of unweighted coupling.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0a9794e42a35d1a0d5e65b8350060fe2973be8d4

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Modelling dynamical processes in complex socio-technical systems (2011, background): “For instance, in the area of synchronization it has been shown that networks with heavy-tailed degree distributions, and therefore a large number of hubs, are more difficult to synchronize than homogeneous networks, a counterintuitive insight dubbed the paradox of heterogeneity 63–66 .”
Critical phenomena in complex networks (2007, methodology): “A similar improvement was obtained by using a different, local weighting procedure based on the degrees of the nearest neighbors (Motter et al., 2005c).”
COMPLEX ADAPTIVE SYSTEMS AND THE DIFFUSION OF INNOVATIONS (2005, unknown): “drastically enhanced and may become positively correlated with heterogeneity,” potentially reducing the costs involved in the creation of effective network ties ( Motter, Zhou and Kurths, 2005, p. 334 ).”
GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, background): “, 2005), local efficiency and global efficiency (Latora and Marchiori, 2001, 2003), modularity (Newman, 2006), assortativity (Newman, 2002; Leung and Chau, 2007), synchronization (Barahona and Pecora, 2002; Motter et al., 2005) and hierarchy (Ravasz and Barabási, 2003).”
Synchronization in complex networks (2008, methodology): “The first works on synchronization in weighted networks considered that the weighted input of a node i from a node j depends on the degree ki of node i (Motter et al., 2005a,b), with a model of weighted coupling as wij = k θ i , so that the matrix G = (Gij) in Eq.”
Explosive transitions in complex networks’ structure and dynamics: Percolation and synchronization (2016, background): “[259, 260], enhanced synchronization is obtained by normalizing the coupling to the power of the node degree ki (i.”
The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives (2024, background): “Disassortative networks are more vulnerable to perturbations and appear to be easier to synchronize than assortative networks (Motter et al., 2005; di Bernardo et al., 2007).”