EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Mitochondrial Network Size Scaling in Budding Yeast

Mitochondria must grow with the growing cell to ensure proper cellular physiology and inheritance upon division. We measured the physical size of mitochondrial networks in budding yeast and found that mitochondrial network size increased with increasing cell size and that this scaling relation occurred primarily in the bud. The mitochondria-to-cell size ratio continually decreased in aging mothers over successive generations. However, regardless of the mother's age or mitochondrial content, all buds attained the same average ratio. Thus, yeast populations achieve a stable scaling relation between mitochondrial content and cell size despite asymmetry in inheritance.

Social network size and perceived network sufficiency as predictors of perceived health status

This study focused on the relationship between social network size (number of friends and relatives), perceived sufficiency of the network and self-rated health utilizing data from the National Survey of Personal Health Practices and Consequences, 1979. For men neither perceived sufficiency nor number of relatives were associated with self-rated health status. The number of friends was positively associated with health status. For women perceived network sufficiency was positively and significantly related to health status, independent of network size. The number of friends and relatives was not associated with self-rated health status. The sociodemographic variables accounted for most of the explained variance in health status for both males and females. Social networks may hold different meanings for women and men, and may require qualitative as well as quantitative analysis. There may have been insufficient variance in the major variables to produce meaningful results. ^

The influence of connectivity on the firing rate in a neuronal network with electrical and chemical synapses

We study the firing rate properties of a cellular automaton model for a neuronal network with chemical synapses. We propose a simple mechanism in which the nonlocal connections are included, through electrical and chemical synapses. In the latter case, we introduce a time delay which produces self-sustained activity. Nonlocal connections, or shortcuts, are randomly introduced according to a specified connection probability. There is a range of connection probabilities for which neuron firing occurs, as well as a critical probability for which the firing ceases in the absence of time delay. The critical probability for nonlocal shortcuts depends on the network size according to a power-law. We also compute the firing rate amplification factor by varying both the connection probability and the time delay for different network sizes. (C) 2011 Elsevier B.V. All rights reserved.

Approximate entropy of network parameters

We study the notion of approximate entropy within the framework of network theory. Approximate entropy is an uncertainty measure originally proposed in the context of dynamical systems and time series. We first define a purely structural entropy obtained by computing the approximate entropy of the so-called slide sequence. This is a surrogate of the degree sequence and it is suggested by the frequency partition of a graph. We examine this quantity for standard scale-free and Erdös-Rényi networks. By using classical results of Pincus, we show that our entropy measure often converges with network size to a certain binary Shannon entropy. As a second step, with specific attention to networks generated by dynamical processes, we investigate approximate entropy of horizontal visibility graphs. Visibility graphs allow us to naturally associate with a network the notion of temporal correlations, therefore providing the measure a dynamical garment. We show that approximate entropy distinguishes visibility graphs generated by processes with different complexity. The result probes to a greater extent these networks for the study of dynamical systems. Applications to certain biological data arising in cancer genomics are finally considered in the light of both approaches.

Inherent size constraints on prokaryote gene networks due to "accelerating" growth

Networks exhibiting accelerating growth have total link numbers growing faster than linearly with network size and either reach a limit or exhibit graduated transitions from nonstationary-to-stationary statistics and from random to scale-free to regular statistics as the network size grows. However, if for any reason the network cannot tolerate such gross structural changes then accelerating networks are constrained to have sizes below some critical value. This is of interest as the regulatory gene networks of single-celled prokaryotes are characterized by an accelerating quadratic growth and are size constrained to be less than about 10,000 genes encoded in DNA sequence of less than about 10 megabases. This paper presents a probabilistic accelerating network model for prokaryotic gene regulation which closely matches observed statistics by employing two classes of network nodes (regulatory and non-regulatory) and directed links whose inbound heads are exponentially distributed over all nodes and whose outbound tails are preferentially attached to regulatory nodes and described by a scale-free distribution. This model explains the observed quadratic growth in regulator number with gene number and predicts an upper prokaryote size limit closely approximating the observed value. (c) 2005 Elsevier GmbH. All rights reserved.

Analysis and comparison of network performance with different network parameters

The purpose of this study was to analyze the network performance by observing the effect of varying network size and data link rate on one of the most commonly found network configurations. Computer networks have been growing explosively. Networking is used in every aspect of business, including advertising, production, shipping, planning, billing, and accounting. Communication takes place through networks that form the basis of transfer of information. The number and type of components may vary from network to network depending on several factors such as requirement and actual physical placement of the networks. There is no fixed size of the networks and they can be very small consisting of say five to six nodes or very large consisting of over two thousand nodes. The varying network sizes make it very important to study the network performance so as to be able to predict the functioning and the suitability of the network. The findings demonstrated that the network performance parameters such as global delay, load, router processor utilization, router processor delay, etc. are affected. The findings demonstrated that the network performance parameters such as global delay, load, router processor utilization, router processor delay, etc. are affected significantly due to the increase in the size of the network and that there exists a correlation between the various parameters and the size of the network. These variations are not only dependent on the magnitude of the change in the actual physical area of the network but also on the data link rate used to connect the various components of the network.

Inference of gene regulatory networks from time series by Tsallis entropy

Background: The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results: In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions: A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 <= q <= 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/.

Scale-free networks and scalable interdomain routing

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Assessing the role of social networks on migrant labor market outcomes: Evidence from a representative immigrant survey

What role do social networks play in determining migrant labor market outcomes? We examine this question using data from a random sample of 1500 immigrants living in Ireland. We propose a theoretical model formally predicting that immigrants with more contacts have additional access to job offers, and are therefore better able to become employed and choose higher paid jobs. Our empirical analysis confirms these findings, while focusing more generally on the relationship between migrants’ social networks and a variety of labor market outcomes (namely wages, employment, occupational choice and job security), contrary to the literature. We find evidence that having one more contact in the network is associated with an increase of 11pp in the probability of being employed and with an increase of about 100 euros in the average salary. However, our data is not suggestive of a network size effect on occupational choice and job security. Our findings are robust to sample selection and other endogeneity concerns.

Assessing the role of social networks on migrant labor market outcomes: Evidence from a representative immigrant survey

What role do social networks play in determining migrant labor market outcomes? We examine this question using data from a random sample of 1500 immigrants living in Ireland. We propose a theoretical model formally predicting that immigrants with more contacts have additional access to job offers, and are therefore better able to become employed and choose higher paid jobs. Our empirical analysis confirms these findings, while focusing more generally on the relationship between migrants’ social networks and a variety of labor market outcomes (namely wages, employment, occupational choice and job security), contrary to the literature. We find evidence that having one more contact in the network is associated with an increase of 11pp in the probability of being employed and with an increase of about 100 euros in the average salary. However, our data is not suggestive of a network size effect on occupational choice and job security. Our findings are robust to sample selection and other endogeneity concerns.

A survey of distributed data aggregation algorithms

Distributed data aggregation is an important task, allowing the de- centralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting val- ues result from the distributed computation of functions like count, sum and average. Some application examples can found to determine the network size, total storage capacity, average load, majorities and many others. In the last decade, many di erent approaches have been pro- posed, with di erent trade-o s in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of ag- gregation algorithms, it can be di cult and time consuming to determine which techniques will be more appropriate to use in speci c settings, jus- tifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally de nes the concept of aggrega- tion, characterizing the di erent types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.

Congested hubs

Hub congestion is a major problem and a relevant policy issue because it causes delays and many organizational problems at airports that end up implying unpleasant consequences both for air travelers and airlines. In a competitive framework in which carriers choose aircraft size, this paper suggests that airlines schedule too many flights using overly small aircraft, which constitutes a major contributor to congestion. Two- part congestion tolls, accounting for the congestion imposed on other carriers and the congestion imposed on all passengers, are needed to recover e¢ ciency. Finally, we analyze the validity of the results by studying the effects of network size, airport capacity, competition in layover time, and the formation of airline alliances. Keywords: congestion; hub-and-spoke networks; overprovision of frequency; con- gestion internalization; congestion tolls JEL Classiffication Numbers: L13; L2; L93

On the distribution of income and worker assignment under intra-firm spillovers, with an application to ideas and networks

We study the earnings structure and the equilibrium assignment of workers when workers exert intra-firm spillovers on each other.We allow for arbitrary spillovers provided output depends on some aggregate index of workers' skill. Despite the possibility of increasing returns to skills, equilibrium typically exists. We show that equilibrium will typically be segregated; that the skill space can be partitioned into a set of segments and any firm hires from only one segment. Next, we apply the model to analyze the effect of information technology on segmentation and the distribution of income. There are two types of human capital, productivity and creativity, i.e. the ability to produce ideas that may be duplicated over a network. Under plausible assumptions, inequality rises and then falls when network size increases, and the poorest workers cannot lose. We also analyze the impact of an improvement in worker quality and of an increased international mobility of ideas.

Développement des compétences et des cerlces sociaux à l'adolescence : conséquances des interactions médiatisées par ordinateur

Ce travail porte sur le développement des compétences sociales et des cercles sociaux à l'adolescence. Il s'intéresse plus particulièrement à l'effet au cours du temps que peuvent avoir les nouveaux moyens de communication électronique (MCE) sur ces deux aspects de la socialisation. Trois parties principales se dégagent de ce travail. La première partie présente le développement d'un outil multidimensionnel destiné à l'évaluation des compétences sociales et permettant de les distinguer en fonction du contexte d'interaction (online vs. offline). La seconde partie porte sur la comparaison des compétences sociales et de la taille du cercle social de 329 adolescents de 7e et de 8e année primaire, en fonction de leur utilisation, ou non, des MCE. Elle met en évidence que ces deux aspects diffèrent de manière statistiquement significative entre les deux groupes, en faveur des utilisateurs de MCE. La troisième partie se centre sur l'utilisation de différents MCE et sur les effets différentiels qu'ils peuvent avoir au cours du temps sur ces deux aspects de la socialisation. Les analyses des données longitudinales mettent en évidence que l'utilisation de sites de réseaux sociaux (SRS) est particulièrement susceptible d'améliorer les compétences sociales et d'augmenter la taille du cercle social. Un modèle dans lequel les compétences sociales jouent un rôle de médiateur entre l'utilisation de SRS et la taille du cercle social est finalement postulé. - This work's topic concerns the development of social skills and social network size during adolescence. It examines more particularly the effects that new online communication media (OCM) may have on these two aspects of adolescent socialization. It is subdivided in three distinct parts. The first part presents the development of a multidimensional tool designed to assess social skills in two different contexts of interaction (online vs. offline). The second part compares the social skills and social network size of 329 adolescents depending on their use, or not, of OCM. It highlights significant differences on these two aspects between users and non-users, in favor of OCM users. The third part focuses on the differential effects that six OCM may have over time on these two aspects of socialization. Longitudinal data analyses highlight that the use of social network sites (SNS) is particularly likely to improve social skills and to increase social network size. A model in which social skills mediate the relationship between SNS use and social network size is finally postulated.