Sensitivity of complex networks measurements

Complex networks obtained from real-world networks are often characterized by incompleteness and noise, consequences of imperfect sampling as well as artifacts in the acquisition process. Because the characterization, analysis and modeling of complex systems underlain by complex networks are critically affected by the quality and completeness of the respective initial structures, it becomes imperative to devise methodologies for identifying and quantifying the effects of the sampling on the network structure. One way to evaluate these effects is through an analysis of the sensitivity of complex network measurements to perturbations in the topology of the network. In this paper, measurement sensibility is quantified in terms of the relative entropy of the respective distributions. Three particularly important kinds of progressive perturbations to the network are considered, namely, edge suppression, addition and rewiring. The measurements allowing the best balance of stability (smaller sensitivity to perturbations) and discriminability (separation between different network topologies) are identified with respect to each type of perturbation. Such an analysis includes eight different measurements applied on six different complex networks models and three real-world networks. This approach allows one to choose the appropriate measurements in order to obtain accurate results for networks where sampling bias cannot be avoided-a very frequent situation in research on complex networks.

A multi-GPU algorithm for large-scale neuronal networks

Large-scale simulations of parts of the brain using detailed neuronal models to improve our understanding of brain functions are becoming a reality with the usage of supercomputers and large clusters. However, the high acquisition and maintenance cost of these computers, including the physical space, air conditioning, and electrical power, limits the number of simulations of this kind that scientists can perform. Modern commodity graphical cards, based on the CUDA platform, contain graphical processing units (GPUs) composed of hundreds of processors that can simultaneously execute thousands of threads and thus constitute a low-cost solution for many high-performance computing applications. In this work, we present a CUDA algorithm that enables the execution, on multiple GPUs, of simulations of large-scale networks composed of biologically realistic Hodgkin-Huxley neurons. The algorithm represents each neuron as a CUDA thread, which solves the set of coupled differential equations that model each neuron. Communication among neurons located in different GPUs is coordinated by the CPU. We obtained speedups of 40 for the simulation of 200k neurons that received random external input and speedups of 9 for a network with 200k neurons and 20M neuronal connections, in a single computer with two graphic boards with two GPUs each, when compared with a modern quad-core CPU. Copyright (C) 2010 John Wiley & Sons, Ltd.

Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network

Differently from theoretical scale-free networks, most real networks present multi-scale behavior, with nodes structured in different types of functional groups and communities. While the majority of approaches for classification of nodes in a complex network has relied on local measurements of the topology/connectivity around each node, valuable information about node functionality can be obtained by concentric (or hierarchical) measurements. This paper extends previous methodologies based on concentric measurements, by studying the possibility of using agglomerative clustering methods, in order to obtain a set of functional groups of nodes, considering particular institutional collaboration network nodes, including various known communities (departments of the University of Sao Paulo). Among the interesting obtained findings, we emphasize the scale-free nature of the network obtained, as well as identification of different patterns of authorship emerging from different areas (e.g. human and exact sciences). Another interesting result concerns the relatively uniform distribution of hubs along concentric levels, contrariwise to the non-uniform pattern found in theoretical scale-free networks such as the BA model. (C) 2008 Elsevier B.V. All rights reserved.

MODELING THE EVOLUTION OF COMPLEX NETWORKS THROUGH THE PATH-STAR TRANSFORMATION AND OPTIMAL MULTIVARIATE METHODS

The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modi. cations in their intrinsic organization. For instance, the network of transportation routes between cities and towns ( hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways ( paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation ( in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.

Seeking for simplicity in complex networks

Complex networks can be understood as graphs whose connectivity properties deviate from those of regular or near-regular graphs, which are understood as being ""simple"". While a great deal of the attention so far dedicated to complex networks has been duly driven by the ""complex"" nature of these structures, in this work we address the identification of their simplicity. The basic idea is to seek for subgraphs whose nodes exhibit similar measurements. This approach paves the way for complementing the characterization of networks, including results suggesting that the protein-protein interaction networks, and to a lesser extent also the Internet, may be getting simpler over time. Copyright (C) EPLA, 2009

Border trees of complex networks

The comprehensive characterization of the structure of complex networks is essential to understand the dynamical processes which guide their evolution. The discovery of the scale-free distribution and the small-world properties of real networks were fundamental to stimulate more realistic models and to understand important dynamical processes related to network growth. However, the properties of the network borders (nodes with degree equal to 1), one of its most fragile parts, remained little investigated and understood. The border nodes may be involved in the evolution of structures such as geographical networks. Here we analyze the border trees of complex networks, which are defined as the subgraphs without cycles connected to the remainder of the network (containing cycles) and terminating into border nodes. In addition to describing an algorithm for identification of such tree subgraphs, we also consider how their topological properties can be quantified in terms of their depth and number of leaves. We investigate the properties of border trees for several theoretical models as well as real-world networks. Among the obtained results, we found that more than half of the nodes of some real-world networks belong to the border trees. A power-law with cut-off was observed for the distribution of the depth and number of leaves of the border trees. An analysis of the local role of the nodes in the border trees was also performed.

EFFECTIVE NETWORKS FOR REAL-TIME DISTRIBUTED PROCESSING

This paper applies the concepts and methods of complex networks to the development of models and simulations of master-slave distributed real-time systems by introducing an upper bound in the allowable delivery time of the packets with computation results. Two representative interconnection models are taken into account: Uniformly random and scale free (Barabasi-Albert), including the presence of background traffic of packets. The obtained results include the identification of the uniformly random interconnectivity scheme as being largely more efficient than the scale-free counterpart. Also, increased latency tolerance of the application provides no help under congestion.

Estimating complex cortical networks via surface recordings-A critical note

We discuss potential caveats when estimating topologies of 3D brain networks from surface recordings. It is virtually impossible to record activity from all single neurons in the brain and one has to rely on techniques that measure average activity at sparsely located (non-invasive) recording sites Effects of this spatial sampling in relation to structural network measures like centrality and assortativity were analyzed using multivariate classifiers A simplified model of 3D brain connectivity incorporating both short- and long-range connections served for testing. To mimic M/EEG recordings we sampled this model via non-overlapping regions and weighted nodes and connections according to their proximity to the recording sites We used various complex network models for reference and tried to classify sampled versions of the ""brain-like"" network as one of these archetypes It was found that sampled networks may substantially deviate in topology from the respective original networks for small sample sizes For experimental studies this may imply that surface recordings can yield network structures that might not agree with its generating 3D network. (C) 2010 Elsevier Inc All rights reserved

SZNAJD MODEL AND PROPORTIONAL ELECTIONS: THE ROLE OF THE TOPOLOGY OF THE NETWORK

The Sznajd model (SM) has been employed with success in the last years to describe opinion propagation in a community. In particular, it has been claimed that its transient is able to reproduce some scale properties observed in data of proportional elections, in different countries, if the community structure (the network) is scale-free. In this work, we investigate the properties of the transient of a particular version of the SM, introduced by Bernardes and co-authors in 2002. We studied the behavior of the model in networks of different topologies through the time evolution of an order parameter known as interface density, and concluded that regular lattices with high dimensionality also leads to a power-law distribution of the number of candidates with v votes. Also, we show that the particular absorbing state achieved in the stationary state (or else, the winner candidate), is related to a particular feature of the model, that may not be realistic in all situations.

From decoherence-free channels to decoherence-free and quasi-free subspaces within bosonic dissipative networks

We present a technique to build, within a dissipative bosonic network, decoherence-free channels (DFCs): a group of normal-mode oscillators with null effective damping rates. We verify that the states protected within the DFC define the well-known decoherence-free subspaces (DFSs) when mapped back into the natural network oscillators. Therefore, our technique to build protected normal-mode channels turns out to be an alternative way to build DFSs, which offers advantages over the conventional method. It enables the computation of all the network-protected states at once, as well as leading naturally to the concept of the decoherence quasi-free subspace (DQFS), inside which a superposition state is quasi-completely protected against decoherence. The concept of the DQFS, weaker than that of the DFS, may provide a more manageable mechanism to control decoherence. Finally, as an application of the DQFSs, we show how to build them for quasi-perfect state transfer in networks of coupled quantum dissipative oscillators.

Resilience of protein-protein interaction networks as determined by their large-scale topological features

The relationship between the structure and function of biological networks constitutes a fundamental issue in systems biology. Particularly, the structure of protein-protein interaction networks is related to important biological functions. In this work, we investigated how such a resilience is determined by the large scale features of the respective networks. Four species are taken into account, namely yeast Saccharomyces cerevisiae, worm Caenorhabditis elegans, fly Drosophila melanogaster and Homo sapiens. We adopted two entropy-related measurements (degree entropy and dynamic entropy) in order to quantify the overall degree of robustness of these networks. We verified that while they exhibit similar structural variations under random node removal, they differ significantly when subjected to intentional attacks (hub removal). As a matter of fact, more complex species tended to exhibit more robust networks. More specifically, we quantified how six important measurements of the networks topology (namely clustering coefficient, average degree of neighbors, average shortest path length, diameter, assortativity coefficient, and slope of the power law degree distribution) correlated with the two entropy measurements. Our results revealed that the fraction of hubs and the average neighbor degree contribute significantly for the resilience of networks. In addition, the topological analysis of the removed hubs indicated that the presence of alternative paths between the proteins connected to hubs tend to reinforce resilience. The performed analysis helps to understand how resilience is underlain in networks and can be applied to the development of protein network models.

Climate perspective on the large-scale circulation associated with the transition of the first South Atlantic hurricane

The landfall of Cyclone Catarina on the Brazilian coast in March 2004 became known as the first documented hurricane in the South Atlantic Ocean, promoting a new view oil how large-scale features can contribute to tropical transition. The aim of this paper is to put the large-scale circulation associated with Catarina`s transition in climate perspective. This is discussed in the light of a robust pattern of spatial correlations between thermodynamic and dynamic variables of importance for hurricane formation. A discussion on how transition mechanisms respond to the present-day circulation is presented. These associations help in understanding why Catarina was formed in a region previously thought to be hurricane-free. Catarina developed over a large-scale area of thermodynamically favourable air/sea temperature contrast. This aspect explains the paradox that such a rare system developed when the sea surface temperature was slightly below average. But, although thermodynamics played an important role, it is apparent that Catarina would not have formed without the key dynamic interplay triggered by a high latitude blocking. The blocking was associated with an extreme positive phase of the Southern Annular Mode (SAM) both hemispherically and locally, and the nearby area where Catarina developed is found to be more cyclonic during the positive phase of the SAM. A conceptual model is developed and a `South Atlantic index` is introduced as a useful diagnostic of potential conditions leading to tropical transition in the area, where large-scale indices indicate trends towards more favourable atmospheric conditions for tropical cyclone formation. Copyright (c) 2008 Royal Meteorological Society

Scalable model-based configuration management of security services in complex enterprise networks

Security administrators face the challenge of designing, deploying and maintaining a variety of configuration files related to security systems, especially in large-scale networks. These files have heterogeneous syntaxes and follow differing semantic concepts. Nevertheless, they are interdependent due to security services having to cooperate and their configuration to be consistent with each other, so that global security policies are completely and correctly enforced. To tackle this problem, our approach supports a comfortable definition of an abstract high-level security policy and provides an automated derivation of the desired configuration files. It is an extension of policy-based management and policy hierarchies, combining model-based management (MBM) with system modularization. MBM employs an object-oriented model of the managed system to obtain the details needed for automated policy refinement. The modularization into abstract subsystems (ASs) segment the system-and the model-into units which more closely encapsulate related system components and provide focused abstract views. As a result, scalability is achieved and even comprehensive IT systems can be modelled in a unified manner. The associated tool MoBaSeC (Model-Based-Service-Configuration) supports interactive graphical modelling, automated model analysis and policy refinement with the derivation of configuration files. We describe the MBM and AS approaches, outline the tool functions and exemplify their applications and results obtained. Copyright (C) 2010 John Wiley & Sons, Ltd.

Liquid crystalline cellulosic elastomers: free standing anisotropic films under stretching

The structure and local ordering of 1,6-hexamethylenediisocyanate-(acetoxypropy1) cellulose (HDI-APC) liquid crystalline elastomer thin films are investigated by using X-ray diffraction and scattering techniques. Optical microscopy and mechanical essays are performed to complement the investigation. The study is performed in films subjected or not to an uniaxial stress. Our results indicate that the film is constituted by a bundle of helicoidal fiber-like structure, where the cellobiose block spins around the axis of the fiber, like a string-structure in a smectic-like packing, with the pitch defined by a smectic-like layer. The fibers are in average perpendicular to the smectic-like planes. Without the stretch, these bundles are warped, only with a residual orientation along the casting direction. The stretch orients the bundles along it, increasing the smectic-like and the nematic-like ordering of the fibers. Under stress, the network of molecules which connects the cellobiose blocs and forms the cellulosic matrix tends to organize their links in a hexagonal-like structure with lattice parameter commensurate to the smectic-like structure.

Solvatochromism in Binary Mixtures: First Report on a Solvation Free Energy Relationship between Solvent Exchange Equilibrium Constants and the Properties of the Medium

We have employed UV-vis spectroscopy in order to investigate details of the solvation of six solvatochromic indicators, hereafter designated as ""probes"", namely, 2,6-diphenyl-4-(2,4,6-triphenylpyridinium-1-yl) phenolate (RB); 4-[(E)-2-(1-methylpyridinium-4-yl)ethenyl] phenolate, MePM; 1-methylquinolinium-8-olate, QB; 2-bromo-4-[(E)-2-(1-methylpyridinium-4-yl)ethenyl] phenolate, MePMBr, 2,6-dichloro-4-(2,4,6-triphenylpyridinium-1-yl) phenolate (WB); and 2,6-dibromo-4-[(E)-2-(1-methylpyridinium-4-yl)ethenyl] phenolate, MePMBr,, respectively. These can be divided into three pairs, each includes two probes of similar pK(a) in water and different lipophilicity. Solvation has been studied in binary mixtures, BMs, of water, W, with 12 protic organic solvents, S, including mono- and bifunctional alcohols (2-alkoxyethanoles, unsaturated and chlorinated alcohols). Each medium was treated as a mixture of S, W, and a complex solvent, S-W, formed by hydrogen bonding. Values of lambda(max) (of the probe intramolecular charge transfer) were converted into empirical polarity scales, E(T)(probe) in kcal/mol, whose values were correlated with the effective mole fraction of water in the medium, chi w(effective). This correlation furnished three equilibrium constants for the exchange of solvents in the probe solvation shell; phi(W/S) (W substitutes S): phi(S-W/W) (S-W substitutes W), and phi(S-W/S) (S-W substitutes S), respectively. The values of these constants depend on the physicochemical properties of the probe and the medium. We tested, for the first time, the applicability of a new solvation free energy relationship: phi = constant + a alpha(BM) + b beta(BM) + s(pi*(BM) + d delta) + p log P(BM), where a, b, s, and p are regression coefficients alpha(BM), beta(BM), and pi*(BM) are solvatochromic parameters of the BM, delta is a correction term for pi*, and log P is an empirical scale of lipophilicity. Correlations were carried out with two-, three-, and four-medium descriptors. In all cases, three descriptors gave satisfactory correlations; use of four parameters gave only a marginal increase of the goodness of fit. For phi(W/S), the most important descriptor was found to be the lipophilicity of the medium; for phi(S-W/W) and phi(S-W/S), solvent basicity is either statistically relevant or is the most important descriptor. These responses are different from those of E(T)(probe) of many solvatochromic indicators in pure solvents, where the importance of solvent basicity is usually marginal, and can be neglected.