Transparency effect in the emergence of monopolies in social networks

Power law degree distribution was shown in many complex networks. However, in most real systems, deviation from power-law behavior is observed in social and economical networks and emergence of giant hubs is obvious in real network structures far from the tail of power law. We propose a model based on the information transparency (transparency means how much the information is obvious to others). This model can explain power structure in societies with non-transparency in information delivery. The emergence of ultra powerful nodes is explained as a direct result of censorship. Based on these assumptions, we define four distinct transparency regions: perfect non-transparent, low transparent, perfect transparent and exaggerated regions. We observe the emergence of some ultra powerful (very high degree) nodes in low transparent networks, in accordance with the economical and social systems. We show that the low transparent networks are more vulnerable to attacks and the controllability of low transparent networks is harder than the others. Also, the ultra powerful nodes in the low transparent networks have a smaller mean length and higher clustering coefficients than the other regions.Comment: 14 Pages, 3 figure

Synchronization in complex networks

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.Comment: Final version published in Physics Reports. More information available at http://synchronets.googlepages.com

Rethinking network reciprocity over social ties: local interactions make direct reciprocity possible and pave the rational way to cooperation

Since Nowak & May's (1992) influential paper, network reciprocity--the fact that individuals' interactions repeated within a local neighborhood support the evolution of cooperation--has been confirmed in several theoretical models. Essentially, local interactions allow cooperators to stay protected from exploiters by assorting into clusters, and the heterogeneity of the network of contacts--the co-presence of low- and high-connected nodes--has been shown to further favor cooperation. The few available large-scale experiments on humans have however missed these effects. The reason is that, while models assume that individuals update strategy by imitating better performing neighbors, experiments showed that humans are more prone to reciprocate cooperation than to compare payoffs. Inspired by the empirical results, we rethink network reciprocity as a rational form of direct reciprocity on networks--networked rational reciprocity--indeed made possible by the locality of interactions. We show that reciprocal altruism in a networked prisoner's dilemma can invade and fixate in any network of rational agents, profit-maximizing over an horizon of future interactions. We find that networked rational reciprocity works better at low average connectivity and we unveil the role of network heterogeneity. Only if cooperating hubs invest in the initial cost of exploitation, the invasion of cooperation is boosted; it is otherwise hindered. Although humans might not be as rational as here assumed, our results could help the design and interpretation of new experiments in social and economic network

Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images

We propose a novel scheme for designing fuzzy rule based classifier. An SOFM based method is used for generating a set of prototypes which is used to generate a set of fuzzy rules. Each rule represents a region in the feature space that we call the context of the rule. The rules are tuned with respect to their context. We justified that the reasoning scheme may be different in different context leading to context sensitive inferencing. To realize context sensitive inferencing we used a softmin operator with a tunable parameter. The proposed scheme is tested on several multispectral satellite image data sets and the performance is found to be much better than the results reported in the literature.Comment: 23 pages, 7 figure

Engineering Robust and Programmable Biological Systems

The ability to engineer programmable biological systems using complex artificial gene networks has great potential to unlock important innovative solutions to many biotechnological challenges. While cells have been engineered to implement complex information processing algorithms and to produce food, materials, and pharmaceuticals, many innovative applications are yet to be realized due to our poor understanding of how robust, reliable, and predictable artificial gene circuits are built. In this work, we demonstrate that robust complex cellular behaviors (e.g., bistability and gene expression dynamics) can be achieved by engineering gene regulatory architecture and increasing the complexity of genetic networks. We further demonstrate that increasing demand for cellular resources causes resource-associated interference among noninteracting genetic devices of various complexities. Importantly, we show that feedback systems can be engineered to enhance the robustness and reliability of genetic circuits by reducing such resource-associated interference among independent circuits. Taken together, this work contributes to understanding the design principles that govern biological robustness and represents an important step towards construction of robust, tunable, reliable, and predictable complex artificial genetic circuits for a wide range of biotechnological applications

Connections Between Adaptive Control and Optimization in Machine Learning

This paper demonstrates many immediate connections between adaptive control and optimization methods commonly employed in machine learning. Starting from common output error formulations, similarities in update law modifications are examined. Concepts in stability, performance, and learning, common to both fields are then discussed. Building on the similarities in update laws and common concepts, new intersections and opportunities for improved algorithm analysis are provided. In particular, a specific problem related to higher order learning is solved through insights obtained from these intersections.Comment: 18 page

Invited review: Epidemics on social networks

Since its first formulations almost a century ago, mathematical models for disease spreading contributed to understand, evaluate and control the epidemic processes.They promoted a dramatic change in how epidemiologists thought of the propagation of infectious diseases.In the last decade, when the traditional epidemiological models seemed to be exhausted, new types of models were developed.These new models incorporated concepts from graph theory to describe and model the underlying social structure.Many of these works merely produced a more detailed extension of the previous results, but some others triggered a completely new paradigm in the mathematical study of epidemic processes. In this review, we will introduce the basic concepts of epidemiology, epidemic modeling and networks, to finally provide a brief description of the most relevant results in the field.Comment: 17 pages, 13 figure