Showing papers by "Scuola superiore di Catania published in 2008"

Complex cooperative networks from evolutionary preferential attachment.

Abstract: In spite of its relevance to the origin of complex networks, the interplay between form and function and its role during network formation remains largely unexplored. While recent studies introduce dynamics by considering rewiring processes of a pre-existent network, we study network growth and formation by proposing an evolutionary preferential attachment model, its main feature being that the capacity of a node to attract new links depends on a dynamical variable governed in turn by the node interactions. As a specific example, we focus on the problem of the emergence of cooperation by analyzing the formation of a social network with interactions given by the Prisoner's Dilemma. The resulting networks show many features of real systems, such as scale-free degree distributions, cooperative behavior and hierarchical clustering. Interestingly, results such as the cooperators being located mostly on nodes of intermediate degree are very different from the observations of cooperative behavior on static networks. The evolutionary preferential attachment mechanism points to an evolutionary origin of scale-free networks and may help understand similar feedback problems in the dynamics of complex networks by appropriately choosing the game describing the interaction of nodes.

Enhancement of cooperation in highly clustered scale-free networks.

Abstract: We study the effect of clustering on the organization of cooperation by analyzing the evolutionary dynamics of the "Prisoner's Dilemma" on scale-free networks with a tunable value of clustering. We find, on the one hand, that a high value of the clustering coefficient produces an overall enhancement of cooperation in the network, even for a very high temptation to defect. On the other hand, high clustering homogenizes the process of invasion of degree classes by defectors, decreasing the chances of survival of low densities of cooperators in the network.

Entropy rate of diffusion processes on complex networks.

Abstract: We introduce the concept of entropy rate to characterize a diffusion process on a complex network. The entropy rate represents the minimal amount of information necessary to describe the diffusion on the network, and is a quantity extremely sensitive to the network topology and dynamics. By opportunely tuning the kind of diffusion, the entropy rate allows one to extract different properties of the network structure. Moreover, entropy maximization indicates how to design optimal diffusion processes, providing a new theoretical tool with applications to social, technological, and communication systems.

Spreading of sexually transmitted diseases in heterosexual populations

Abstract: The spread of sexually transmitted diseases (e.g., chlamydia, syphilis, gonorrhea, HIV, etc.) across populations is a major concern for scientists and health agencies. In this context, both the data collection on sexual contact networks and the modeling of disease spreading are intensive contributions to the search for effective immunization policies. Here, the spreading of sexually transmitted diseases on bipartite scale-free graphs, representing heterosexual contact networks, is considered. We analytically derive the expression for the epidemic threshold and its dependence with the system size in finite populations. We show that the epidemic outbreak in bipartite populations, with number of sexual partners distributed as in empirical observations from national sex surveys, takes place for larger spreading rates than for the case in which the bipartite nature of the network is not taken into account. Numerical simulations confirm the validity of the theoretical results. Our findings indicate that the restriction to crossed infections between the two classes of individuals (males and females) has to be taken into account in the design of efficient immunization strategies for sexually transmitted diseases.

Scaling breakdown in flow fluctuations on complex networks.

Abstract: We propose a model of random diffusion to investigate flow fluctuations in complex networks. We derive an analytical law showing that the dependence of fluctuations with the mean traffic in a network is ruled by the delicate interplay of three factors, respectively, of dynamical, topological and statistical nature. In particular, we demonstrate that the existence of a power-law scaling characterizing the flow fluctuations at every node in the network cannot be claimed. We show the validity of this scaling breakdown under quite general topological and dynamical situations by means of different traffic algorithms and by analyzing real data.

Multiple centrality assessment in Parma: a network analysis of paths and open spaces

Abstract: One of the largest of Europe, the recently realized university campus ‘Area of the Sciences’ in Parma, northern Italy, has been planned for a comprehensive programme of renovation and revitalization with a special focus on vehicular accessibility and the quality of open spaces. As part of the problem setting phase, the authors, with Rivi Engineering, applied Multiple Centrality Assessment (MCA) – a process of network analysis based on primal graphs, a set of different centrality indices and the metric computation of distances – in order to understand why the existent system of open spaces and pedestrian paths is so scarcely experienced by students as well as faculty and staff members and why it appears so poorly supportive of social life and human exchange. In the problem-solving phase MCA was also applied, turning out to offer a relevant contribution to the comparative evaluation of two alternative proposed scenarios, leading to the identification of one final solution of urban design. In the present paper, the first professional application of MCA, an innovative approach to the network analysis of geographic complex systems, is presented and its relevance in the context of a problem of urban design illustrated.

Mechanism of boron diffusion in amorphous silicon.

Abstract: We have elucidated the mechanism for B migration in the amorphous (a-) Si network. B diffusivity in a-Si is much higher than in crystalline Si; it is transient and increases with B concentration up to 2 x 10(20) B/cm(3). At higher density, B atoms in a-Si quickly precipitate. B diffusion is indirect, mediated by dangling bonds (DB) present in a-Si. The density of DB is enhanced by B accommodation in the a-Si network and decreases because of a-Si relaxation. Accurate data simulations allow one to extract the DB diffusivity, whose activation energy is 2.6 eV. Implications of these results are discussed.

Extending the definition of modularity to directed graphs with overlapping communities

Abstract: Complex networks topologies present interesting and surprising properties, such as community structures, which can be exploited to optimize communication, to find new efficient and context-aware routing algorithms or simply to understand the dynamics and meaning of relationships among nodes. Complex networks are gaining more and more importance as a reference model and are a powerful interpretation tool for many different kinds of natural, biological and social networks, where directed relationships and contextual belonging of nodes to many different communities is a matter of fact. This paper starts from the definition of modularity function, given by M. Newman to evaluate the goodness of network community decompositions, and extends it to the more general case of directed graphs with overlapping community structures. Interesting properties of the proposed extension are discussed, a method for finding overlapping communities is proposed and results of its application to benchmark case-studies are reported. We also propose a new dataset which could be used as a reference benchmark for overlapping community structures identification.

Community structure of cortical networks in spinal cord injured patients

Abstract: In the present study, we estimated the cortical networks were from high-resolution EEG recordings in a group of spinal cord injured patients and in a group of healthy subjects, during the preparation of a limb movement. Then, we use the Markov Clustering method to analyse the division of the network into community structures. The results indicate large differences between the injured patients and the healthy subjects. In particular, the networks of spinal cord injured patient exhibited a higher density of clusters. In the Alpha (7–12 Hz) frequency band, the two observed largest communities were mainly composed by the cingulate motor areas with the supplementary motor areas, and by the pre-motor areas with the right primary motor area of the foot. This functional separation could reflect the partial alteration in the primary motor areas because of the effects of the spinal cord injury.

Epcast: Controlled Dissemination in Human-Based Wireless Networks Using Epidemic Spreading Models

Abstract: Epidemics-inspired techniques have received huge attention in recent years from the distributed systems and networking communities. These algorithms and protocols rely on probabilistic message replication and redundancy to ensure reliable communication. Moreover, they have been successfully exploited to support group communication in distributed systems, broadcasting, multicasting and information dissemination in fixed and mobile networks. However, in most of the existing work, the probability of infection is determined heuristically, without relying on any analytical model. This often leads to unnecessarily high transmission overheads. In this paper we show that models of epidemic spreading in complex networks can be applied to the problem of tuning and controlling the dissemination of information in wireless ad hoc networks composed of devices carried by individuals, i.e., human-based networks. The novelty of our idea resides in the evaluation and exploitation of the structure of the underlying human network for the automatic tuning of the dissemination process in order to improve the protocol performance. We evaluate the results using synthetic mobility models and real human contacts traces.

Statistical correlations of an anyon liquid at low temperatures

Abstract: Using a proposed generalisation of the pair distribution function for a gas of non-interacting particles obeying fractional exclusion statistics in arbitrary dimensionality, we derive the statistical correlations in the asymptotic limit of vanishing or low temperature. While Friedel-like oscillations are present in nearly all non-bosonic cases at T = 0, they are characterised by exponential damping at low temperature. We discuss the dependence of these features on dimensionality and on the value of the statistical parameter α.

Statistical correlations of an anyon liquid at low temperatures

Abstract: Using a proposed generalization of the pair distribution function for a gas of non-interacting particles obeying fractional exclusion statistics in arbitrary dimensionality, we derive the statistical correlations in the asymptotic limit of vanishing or low temperature. While Friedel-like oscillations are present in nearly all non-bosonic cases at T=0, they are characterized by exponential damping at low temperature. We discuss the dependence of these features on dimensionality and on the value of the statistical parameter alpha.

Synchronization in Random Geometric Graphs

Abstract: In this paper we study the synchronization properties of random geometric graphs. We show that the onset of synchronization takes place roughly at the same value of the order parameter that a random graph with the same size and average connectivity. However, the dependence of the order parameter with the coupling strength indicates that the fully synchronized state is more easily attained in random graphs. We next focus on the complete synchronized state and show that this state is less stable for random geometric graphs than for other kinds of complex networks. Finally, a rewiring mechanism is proposed as a way to improve the stability of the fully synchronized state as well as to lower the value of the coupling strength at which it is achieved. Our work has important implications for the synchronization of wireless networks, and should provide valuable insights for the development and deployment of more efficient and robust distributed synchronization protocols for these systems.

Cricket phonotaxis: simple Lego implementation

Abstract: Female crickets are able to recognise and localise their mates, using a simple neural structure that implements phonotaxis. In this paper we show simple Lego implementation of this biological model.

The Ultimatum Game in Complex Networks

Abstract: We address the problem of how cooperative (altruistic-like) behavior arises in natural and social systems by analyzing an ultimatum game in complex networks. Specifically, three types of players are considered: (a) empathetic, whose aspiration level and offer are equal, (b) pragmatic, who do not distinguish between the different roles and aim to obtain the same benefit, and (c) agents whose aspiration level and offer are independent. We analyze the asymptotic behavior of pure populations on different topologies using two kinds of strategic update rules. Natural selection, which relies on replicator dynamics, and Social Penalty, inspired in the Bak-Sneppen dynamics, in which players are subjected to a social selection rule penalizing not only the less fitted individuals, but also their first neighbors. We discuss the emergence of fairness in the different settings and network topologies.

Cooperative behavior of robots controlled by CNN autowaves

Abstract: In this paper we will discuss in some details how the wave-based approach can be applied to roving robots. Two slightly different implementations are shown and experimental results obtained are discussed. The wave-controlled robots have only local information coming from the on-board cameras, but a simple cooperation strategy shown in the following allows to overcome the limits of local information.