6 research outputs found
A Self-organized model for network evolution
Here we provide a detailed analysis, along with some extensions and additonal
investigations, of a recently proposed self-organised model for the evolution
of complex networks. Vertices of the network are characterised by a fitness
variable evolving through an extremal dynamics process, as in the Bak-Sneppen
model representing a prototype of Self-Organized Criticality. The network
topology is in turn shaped by the fitness variable itself, as in the fitness
network model. The system self-organizes to a nontrivial state, characterized
by a power-law decay of dynamical and topological quantities above a critical
threshold. The interplay between topology and dynamics in the system is the key
ingredient leading to an unexpected behaviour of these quantities
Self-Organization and Complex Networks
In this chapter we discuss how the results developed within the theory of
fractals and Self-Organized Criticality (SOC) can be fruitfully exploited as
ingredients of adaptive network models. In order to maintain the presentation
self-contained, we first review the basic ideas behind fractal theory and SOC.
We then briefly review some results in the field of complex networks, and some
of the models that have been proposed. Finally, we present a self-organized
model recently proposed by Garlaschelli et al. [Nat. Phys. 3, 813 (2007)] that
couples the fitness network model defined by Caldarelli et al. [Phys. Rev.
Lett. 89, 258702 (2002)] with the evolution model proposed by Bak and Sneppen
[Phys. Rev. Lett. 71, 4083 (1993)] as a prototype of SOC. Remarkably, we show
that the results obtained for the two models separately change dramatically
when they are coupled together. This indicates that self-organized networks may
represent an entirely novel class of complex systems, whose properties cannot
be straightforwardly understood in terms of what we have learnt so far.Comment: Book chapter in "Adaptive Networks: Theory, Models and Applications",
Editors: Thilo Gross and Hiroki Sayama (Springer/NECSI Studies on Complexity
Series
Detecting communities in large networks
We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns