1,360 research outputs found

    Adaptive synchronization of dynamics on evolving complex networks

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    We study the problem of synchronizing a general complex network by means of an adaptive strategy in the case where the network topology is slowly time varying and every node receives at each time only one aggregate signal from the set of its neighbors. We introduce an appropriately defined potential that each node seeks to minimize in order to reach/maintain synchronization. We show that our strategy is effective in tracking synchronization as well as in achieving synchronization when appropriate conditions are met.Comment: Accepted for publication on Physical Review Letter

    Effects of the network structural properties on its controllability

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    In a recent paper, it has been suggested that the controllability of a diffusively coupled complex network, subject to localized feedback loops at some of its vertices, can be assessed by means of a Master Stability Function approach, where the network controllability is defined in terms of the spectral properties of an appropriate Laplacian matrix. Following that approach, a comparison study is reported here among different network topologies in terms of their controllability. The effects of heterogeneity in the degree distribution, as well as of degree correlation and community structure, are discussed.Comment: Also available online at: http://link.aip.org/link/?CHA/17/03310

    Effects of variations of load distribution on network performance

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    This paper is concerned with the characterization of the relationship between topology and traffic dynamics. We use a model of network generation that allows the transition from random to scale free networks. Specifically, we consider three different topological types of network: random, scale-free with \gamma = 3, scale-free with \gamma = 2. By using a novel LRD traffic generator, we observe best performance, in terms of transmission rates and delivered packets, in the case of random networks. We show that, even if scale-free networks are characterized by shorter characteristic-path- length (the lower the exponent, the lower the path-length), they show worst performances in terms of communication. We conjecture this could be explained in terms of changes in the load distribution, defined here as the number of shortest paths going through a given vertex. In fact, that distribu- tion is characterized by (i) a decreasing mean (ii) an increas- ing standard deviation, as the networks becomes scale-free (especially scale-free networks with low exponents). The use of a degree-independent server also discriminates against a scale-free structure. As a result, since the model is un- controlled, most packets will go through the same vertices, favoring the onset of congestion.Comment: 4 pages, 4 figures, included in conference proceedings ISCAS 2005, Kobe Japa

    Communication models with distributed transmission rates and buffer sizes

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    The paper is concerned with the interplay between network structure and traffic dynamics in a communications network, from the viewpoint of end-to-end performance of packet transfer. We use a model of network generation that allows the transition from random to scale-free networks. Specifically, we are able to consider three different topologycal types of networks: (a) random; (b) scale-free with \gamma=3; (c) scale free with \gamma=2. We also use an LRD traffic generator in order to reproduce the fractal behavior that is observed in real world data communication. The issue is addressed of how the traffic behavior on the network is influenced by the variable factors of the transmission rates and queue length restrictions at the network vertices. We show that these factors can induce drastic changes in the throughput and delivery time of network performance and are able to counter-balance some undesirable effects due to the topology.Comment: 4 pages, 5 figures, IEEE Symposium on Circuits and Systems, Island of Kos, Greece, 200
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