792 resultados para small-world network


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Multidimensional scaling is applied in order to visualize an analogue of the small-world effect implied by edges having different displacement velocities in transportation networks. Our findings are illustrated for two real-world systems, namely the London urban network (streets and underground) and the US highway network enhanced by some of the main US airlines routes. We also show that the travel time in these two networks is drastically changed by attacks targeting the edges with large displacement velocities. (C) 2011 Elsevier By. All rights reserved.

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In the present study, we propose a theoretical graph procedure to investigate multiple pathways in brain functional networks. By taking into account all the possible paths consisting of h links between the nodes pairs of the network, we measured the global network redundancy R (h) as the number of parallel paths and the global network permeability P (h) as the probability to get connected. We used this procedure to investigate the structural and dynamical changes in the cortical networks estimated from a dataset of high-resolution EEG signals in a group of spinal cord injured (SCI) patients during the attempt of foot movement. In the light of a statistical contrast with a healthy population, the permeability index P (h) of the SCI networks increased significantly (P < 0.01) in the Theta frequency band (3-6 Hz) for distances h ranging from 2 to 4. On the contrary, no significant differences were found between the two populations for the redundancy index R (h) . The most significant changes in the brain functional network of SCI patients occurred mainly in the lower spectral contents. These changes were related to an improved propagation of communication between the closest cortical areas rather than to a different level of redundancy. This evidence strengthens the hypothesis of the need for a higher functional interaction among the closest ROIs as a mechanism to compensate the lack of feedback from the peripheral nerves to the sensomotor areas.

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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

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The influence of the thalamus on the diversity of cortical activations is investigated in terms of the Ising model with respect to progressive levels of cortico-thalamic connectivity. The results show that better diversity is achieved at lower modulation levels, being higher than those obtained with counterpart network models.

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In this work a study of social networks based on analysis of family names is presented. A basic approach to the mathematical formalism of graphs is developed and then main theoretical models for complex networks are presented aiming to support the analysis of surnames networks models. These, in turn, are worked so as to be drawn leading quantities, such as aggregation coefficient, minimum average path length and connectivity distribution. Based on these quantities, it can be stated that surnames networks are an example of complex network, showing important features such as preferential attachment and small-world character

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The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e. g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed.

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Aims: Guided tissue regeneration (GTR) and enamel matrix derivatives (EMD) are two popular regenerative treatments for periodontal infrabony lesions. Both have been used in conjunction with other regenerative materials. We conducted a Bayesian network meta-analysis of randomized controlled trials on treatment effects of GTR, EMD and their combination therapies. Material and Methods: A systematic literature search was conducted using the Medline, EMBASE, LILACS and CENTRAL databases up to and including June 2011. Treatment outcomes were changes in probing pocket depth (PPD), clinical attachment level (CAL) and infrabony defect depth. Different types of bone grafts were treated as one group and so were barrier membranes. Results: A total of 53 studies were included in this review, and we found small differences between regenerative therapies which were non-significant statistically and clinically. GTR and GTR-related combination therapies achieved greater PPD reduction than EMD and EMD-related combination therapies. Combination therapies achieved slightly greater CAL gain than the use of EMD or GTR alone. GTR with BG achieved greatest defect fill. Conclusion: Combination therapies performed better than single therapies, but the additional benefits were small. Bayesian network meta-analysis is a promising technique to compare multiple treatments. Further analysis of methodological characteristics will be required prior to clinical recommendations.

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The transient and equilibrium properties of dynamics unfolding in complex systems can depend critically on specific topological features of the underlying interconnections. In this work, we investigate such a relationship with respect to the integrate-and-fire dynamics emanating from a source node and an extended network model that allows control of the small-world feature as well as the length of the long-range connections. A systematic approach to investigate the local and global correlations between structural and dynamical features of the networks was adopted that involved extensive simulations (one and a half million cases) so as to obtain two-dimensional correlation maps. Smooth, but diverse surfaces of correlation values were obtained in all cases. Regarding the global cases, it has been verified that the onset avalanche time (but not its intensity) can be accurately predicted from the structural features within specific regions of the map (i.e. networks with specific structural properties). The analysis at local level revealed that the dynamical features before the avalanches can also be accurately predicted from structural features. This is not possible for the dynamical features after the avalanches take place. This is so because the overall topology of the network predominates over the local topology around the source at the stationary state.

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Glasses in the system [Na2S](2/3)[(B2S3)(x)(P2S5)(1-x)](1/3) (0.0 <= x <= 1.0) were prepared by the melt quenching technique, and their properties were characterized by thermal analysis and impedance spectroscopy. Their atomic-level structures were comprehensively characterized by Raman spectroscopy and B-11, P-31, and Na-23 high resolution solid state magic-angle spinning (MAS) NMR techniques. P-31 MAS NMR peak assignments were made by the presence or absence of homonuclear indirect P-31-P-31 spin-spin interactions as detected using homonuclear J-resolved and refocused INADEQUATE techniques. The extent of B-S-P connectivity in the glassy network was quantified by P-31{B-11} and B-11{P-31} rotational echo double resonance spectroscopy. The results clearly illustrate that the network modifier alkali sulfide, Na2S, is not proportionally shared between the two network former components, B and P. Rather, the thiophosphate (P) component tends to attract a larger concentration of network modifier species than predicted by the bulk composition, and this results in the conversion of P2S74-, pyrothiophosphate, Na/P = 2:1, units into PS43-, orthothiophosphate, Na/P = 3:1, groups. Charge balance is maintained by increasing the net degree of polymerization of the thioborate (B) units through the formation of covalent bridging sulfur (BS) units, B S B. Detailed inspection of the B-11 MAS NMR spectra reveals that multiple thioborate units are formed, ranging from neutral BS3/2 groups all the way to the fully depolymerized orthothioborate (BS33-) species. On the basis of these results, a comprehensive and quantitative structural model is developed for these glasses, on the basis of which the compositional trends in the glass transition temperatures (T-g) and ionic conductivities can be rationalized. Up to x = 0.4, the dominant process can be described in a simplified way by the net reaction equation P-1 + B-1 reversible arrow P-0 + B-4, where the superscripts denote the number of BS atoms for the respective network former species. Above x = 0.4, all of the thiophosphate units are of the P-0 type and both pyro-(B-1) and orthothioborate (B-0) species make increasing contributions to the network structure with increasing x. In sharp contrast to the situation in sodium borophosphate glasses, four-coordinated thioborate species are generally less abundant and heteroatomic B-S-P linkages appear to not exist. On the basis of this structural information, compositional trends in the ionic conductivities are discussed in relation to the nature of the charge-compensating anionic species and the spatial distribution of the charge carriers.

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The mechanisms responsible for containing activity in systems represented by networks are crucial in various phenomena, for example, in diseases such as epilepsy that affect the neuronal networks and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided into modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory, through which containment of activity is achieved by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, namely, random, Barabasi-Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not spread out to the entire neuronal network, even when no special topological organization exists.

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The vast majority of known proteins have not yet been experimentally characterized and little is known about their function. The design and implementation of computational tools can provide insight into the function of proteins based on their sequence, their structure, their evolutionary history and their association with other proteins. Knowledge of the three-dimensional (3D) structure of a protein can lead to a deep understanding of its mode of action and interaction, but currently the structures of <1% of sequences have been experimentally solved. For this reason, it became urgent to develop new methods that are able to computationally extract relevant information from protein sequence and structure. The starting point of my work has been the study of the properties of contacts between protein residues, since they constrain protein folding and characterize different protein structures. Prediction of residue contacts in proteins is an interesting problem whose solution may be useful in protein folding recognition and de novo design. The prediction of these contacts requires the study of the protein inter-residue distances related to the specific type of amino acid pair that are encoded in the so-called contact map. An interesting new way of analyzing those structures came out when network studies were introduced, with pivotal papers demonstrating that protein contact networks also exhibit small-world behavior. In order to highlight constraints for the prediction of protein contact maps and for applications in the field of protein structure prediction and/or reconstruction from experimentally determined contact maps, I studied to which extent the characteristic path length and clustering coefficient of the protein contacts network are values that reveal characteristic features of protein contact maps. Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by combining this knowledge with correctly predicted motifs of secondary structure. In the second part of my work I focused on a particular protein structural motif, the coiled-coil, known to mediate a variety of fundamental biological interactions. Coiled-coils are found in a variety of structural forms and in a wide range of proteins including, for example, small units such as leucine zippers that drive the dimerization of many transcription factors or more complex structures such as the family of viral proteins responsible for virus-host membrane fusion. The coiled-coil structural motif is estimated to account for 5-10% of the protein sequences in the various genomes. Given their biological importance, in my work I introduced a Hidden Markov Model (HMM) that exploits the evolutionary information derived from multiple sequence alignments, to predict coiled-coil regions and to discriminate coiled-coil sequences. The results indicate that the new HMM outperforms all the existing programs and can be adopted for the coiled-coil prediction and for large-scale genome annotation. Genome annotation is a key issue in modern computational biology, being the starting point towards the understanding of the complex processes involved in biological networks. The rapid growth in the number of protein sequences and structures available poses new fundamental problems that still deserve an interpretation. Nevertheless, these data are at the basis of the design of new strategies for tackling problems such as the prediction of protein structure and function. Experimental determination of the functions of all these proteins would be a hugely time-consuming and costly task and, in most instances, has not been carried out. As an example, currently, approximately only 20% of annotated proteins in the Homo sapiens genome have been experimentally characterized. A commonly adopted procedure for annotating protein sequences relies on the "inheritance through homology" based on the notion that similar sequences share similar functions and structures. This procedure consists in the assignment of sequences to a specific group of functionally related sequences which had been grouped through clustering techniques. The clustering procedure is based on suitable similarity rules, since predicting protein structure and function from sequence largely depends on the value of sequence identity. However, additional levels of complexity are due to multi-domain proteins, to proteins that share common domains but that do not necessarily share the same function, to the finding that different combinations of shared domains can lead to different biological roles. In the last part of this study I developed and validate a system that contributes to sequence annotation by taking advantage of a validated transfer through inheritance procedure of the molecular functions and of the structural templates. After a cross-genome comparison with the BLAST program, clusters were built on the basis of two stringent constraints on sequence identity and coverage of the alignment. The adopted measure explicity answers to the problem of multi-domain proteins annotation and allows a fine grain division of the whole set of proteomes used, that ensures cluster homogeneity in terms of sequence length. A high level of coverage of structure templates on the length of protein sequences within clusters ensures that multi-domain proteins when present can be templates for sequences of similar length. This annotation procedure includes the possibility of reliably transferring statistically validated functions and structures to sequences considering information available in the present data bases of molecular functions and structures.

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Questo lavoro di tesi tratta il tema delle reti complesse, mostrando i principali modelli di rete complessa quali: il modello Random, il modello Small-World ed il modello Scale-free; si introdurranno alcune metriche usate per descrivere le reti complesse quali la Degree centrality, la Closeness centrality e la Betweenness centrality; si descriveranno i problemi da tenere in considerazione durante la definizione e l’implementazione di algoritmi su grafi; i modelli di calcolo su cui progettare gli algoritmi per risolvere i problemi su grafi; un’analisi prestazionale degli algoritmi proposti per calcolare i valori di Beweenness centrality su grafi di medio-grandi dimensioni. Parte di questo lavoro di tesi è consistito nello sviluppo di LANA, LArge-scale Network Analyzer, un software che permette il calcolo e l’analisi di varie metriche di centralità su grafo.

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In fact, much of the attraction of network theory initially stemmed from the fact that many networks seem to exhibit some sort of universality, as most of them belong to one of three classes: random, scale-free and small-world networks. Structural properties have been shown to translate into different important properties of a given system, including efficiency, speed of information processing, vulnerability to various forms of stress, and robustness. For example, scale-free and random topologies were shown to be...

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Com o escopo de fornecer subsídios para compreender como o processo de colaboração científica ocorre e se desenvolve em uma instituição de pesquisas, particularmente o IPEN, o trabalho utilizou duas abordagens metodológicas. A primeira utilizou a técnica de análise de redes sociais (ARS) para mapear as redes de colaboração científica em P&D do IPEN. Os dados utilizados na ARS foram extraídos da base de dados digitais de publicações técnico-científicas do IPEN, com o auxílio de um programa computacional, e basearam-se em coautoria compreendendo o período de 2001 a 2010. Esses dados foram agrupados em intervalos consecutivos de dois anos gerando cinco redes bienais. Essa primeira abordagem revelou várias características estruturais relacionadas às redes de colaboração, destacando-se os autores mais proeminentes, distribuição dos componentes, densidade, boundary spanners e aspectos relacionados à distância e agrupamento para definir um estado de redes mundo pequeno (small world). A segunda utilizou o método dos mínimos quadrados parciais, uma variante da técnica de modelagem por equações estruturais, para avaliar e testar um modelo conceitual, apoiado em fatores pessoais, sociais, culturais e circunstanciais, para identificar aqueles que melhor explicam a propensão de um autor do IPEN em estabelecer vínculos de colaboração em ambientes de P&D. A partir do modelo consolidado, avaliou-se o quanto ele explica a posição estrutural que um autor ocupa na rede com base em indicadores de ARS. Nesta segunda parte, os dados foram coletados por meio de uma pesquisa de levantamento com a utilização de um questionário. Os resultados mostraram que o modelo explica aproximadamente 41% da propensão de um autor do IPEN em colaborar com outros autores e em relação à posição estrutural de um autor na rede o poder de explicação variou entre 3% e 3,6%. Outros resultados mostraram que a colaboração entre autores do IPEN tem uma correlação positiva com intensidade moderada com a produtividade, da mesma forma que, os autores mais centrais na rede tendem a ampliar a sua visibilidade. Por fim, vários outros indicadores estatísticos bibliométricos referentes à rede de colaboração em P&D do IPEN foram determinados e revelados, como, a média de autores por publicação, média de publicações por autores do IPEN, total de publicações, total de autores e não autores do IPEN, entre outros. Com isso, esse trabalho fornece uma contribuição teórica e empírica aos estudos relacionados à colaboração científica e ao processo de transferência e preservação de conhecimento, assim como, vários subsídios que contribuem para o contexto de tomada de decisão em ambientes de P&D.

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Description based on: July 1994; title from cover.