Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

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The Structure and Function of Complex Networks

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

Measuring the Strangeness of Strange Attractors

Complex networks arise in a wide range of biological and sociotechnical systems. Epidemic spreading is central to our understanding of dynamical processes in complex networks, and is of interest to physicists, mathematicians, epidemiologists, and computer and social scientists. This review presents the main results and paradigmatic models in infectious disease modeling and generalized social contagion processes.

Epidemic processes in complex networks

Cellular automata are used as simple mathematical models to investigate self-organization in statistical mechanics. A detailed analysis is given of "elementary" cellular automata consisting of a sequence of sites with values 0 or 1 on a line, with each site evolving deterministically in discrete time steps according to definite rules involving the values of its nearest neighbors. With simple initial configurations, the cellular automata either tend to homogeneous states, or generate self-similar patterns with fractal dimensions \ensuremath{\simeq} 1.59 or \ensuremath{\simeq} 1.69. With "random" initial configurations, the irreversible character of the cellular automaton evolution leads to several self-organization phenomena. Statistical properties of the structures generated are found to lie in two universality classes, independent of the details of the initial state or the cellular automaton rules. More complicated cellular automata are briefly considered, and connections with dynamical systems theory and the formal theory of computation are discussed.

Statistical mechanics of cellular automata

Diffusion-limited aggregation is consistent with simple scaling. However, strong subdominant terms are present, and these can account for various earlier claims of anomalous scaling. We show this in detail for the case of multiscaling.

Correction to scaling analysis of diffusion-limited aggregation

We investigate a continuum formulation of surface growth following the Kardar-Parisi-Zhang equation [Phys. Rev. Lett. 56, 889 (1986)] with a power-law distribution of the magnitudes of regional advances. This formulation describes Zhang's ballistic-deposition model [J. Phys. (Paris) 51, 2129 (1990)] with power-law noise and possibly recent fluid-displacement experiments. Our exact theory predicts a transition of the scaling behavior from power-law-noise domination to a Gaussian-noise regime as the power increases. An apparent contradiction with previous simulations is due to a logarithmic correction to the scaling at the transition and to anomalous-growth effects. Analogous scaling behaviors are derived for the Edwards-Wilkinson model [Proc. R. Soc. London Ser. A 381, 17 (1982)] with power-law noise

Exact scaling in surface growth with power-law noise

The creation of fractal clusters by diffusion limited aggregation (DLA) is studied by using iterated stochastic conformal maps following the method proposed recently by Hastings and Levitov. The object of interest is the function $\Phi^{(n)}$ which conformally maps the exterior of the unit circle to the exterior of an $n$-particle DLA. The map $\Phi^{(n)}$ is obtained from $n$ stochastic iterations of a function $\phi$ that maps the unit circle to the unit circle with a bump. The scaling properties usually studied in the literature on DLA appear in a new light using this language. The dimension of the cluster is determined by the linear coefficient in the Laurent expansion of $\Phi^{(n)}$, which asymptotically becomes a deterministic function of $n$. We find new relationships between the generalized dimensions of the harmonic measure and the scaling behavior of the Laurent coefficients.

http://arxiv.org/pdf/chao-dyn/9812020.pdf

Leonard M. Sander

Papers

Correction to scaling analysis of diffusion-limited aggregation

Exact scaling in surface growth with power-law noise

Diffusion Limited Aggregation and Iterated Conformal Maps