Complexity & Systems
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Complexity & Systems
Complex systems present problems both in mathematical modelling and philosophical foundations. The study of complex systems represents a new approach to science that investigates how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment. The equations from which models of complex systems are developed generally derive from statistical physics, information theory and non-linear dynamics, and represent organized but unpredictable behaviors of natural systems that are considered fundamentally complex.  wikipedia (en)
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Scooped by Bernard Ryefield
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Scale-free networks as an epiphenomenon of memory

Scale-free networks have small characteristic path lengths, high clustering, and feature a power law in their degree distribution. They can be obtained by the well-known preferential attachment. However, this mechanism is non-local, in the sense that it requires knowledge of the entire graph in order for the graph to be updated. This strongly suggests that in both physical and practical realizations this mechanism is likely to be the epiphenomenon of some spatially local rule. Here, we present a completely local model that features preferential attachment as an emergent property of self-organized dynamics with memory. This model employs a mechanism similar to that used by ants to search for the optimal path as a consequence of the graph bearing more memory wherever more ants have walked. Such a model can also be realized in solid-state circuits, using non-linear passive elements with memory such as memristors, and thus can be tested experimentally. Since memory is a common trait of physical systems, we expect this mechanism to be a typical feature in the formation of real-world networks.

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Rescooped by Bernard Ryefield from Networks and Graphs
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Exactly scale-free scale-free networks

There is mounting evidence of the apparent ubiquity of scale-free networks among complex systems. Many natural and physical systems exhibit patterns of interconnection that conform, approximately, to the structure expected of a scale-free network. We propose an efficient algorithm to generate representative samples from the space of all networks defined by a particular (scale-free) degree distribution. Using this algorithm we are able to systematically explore that space with some surprising results: in particular, we find that preferential attachment growth models do not yield typical realizations and that there is a certain latent structure among such networks --- which we loosely term "hub-centric". We provide a method to generate or remove this latent hub-centric bias --- thereby demonstrating exactly which features of preferential attachment networks are atypical of the broader class of scale free networks. Based on these results we are also able to statistically determine whether experimentally observed networks are really typical realizations of a given degree distribution (scale-free degree being the example which we explore). In so doing we propose a surrogate generation method for complex networks, exactly analogous the the widely used surrogate tests of nonlinear time series analysis.

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