Search

The effect of synaptic plasticity on the synchronization mechanism of the cerebral cortex has been a hot research topic over the past two decades. There are a great deal of literatures on excitatory pyramidal neurons, but the mechanism of interaction between the inhibitory interneurons is still under exploration. In this study, we consider a complex network consisting of excitatory (E) pyramidal neurons and inhibitory (I) interneurons interacting with chemical synapses through spike-timing-dependent plasticity (STDP). To study the effects of eSTDP and iSTDP on synchronization and oscillation behaviors emerged in an excitatory–inhibitory balanced network, we analyzed three different cases, a small-world network of purely excitatory neurons with eSTDP, a small-world network of purely inhibitory neurons with iSTDP and a small-world network with excitatory–inhibitory balanced neurons. By varying the number of inhibitory interneurons, and that of connected edges in a small-world network, and the coupling strength, these networks exhibit different synchronization and oscillation behaviors. We found that the eSTDP facilitates synchronization effectively, while iSTDP has no significant impact on it. In addition, eSTDP and iSTDP restrict the balance of the excitatory–inhibitory balanced neuronal network together and play a fundamental role in maintaining network stability and synchronization. They also can be used to guide the treatment and further research of neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Coherence resonance (CR), stochastic synchronization (SS), and spike-timing-dependent plasticity (STDP) are ubiquitous dynamical processes in biological neural networks. Whether there exists an optimal network and STDP configuration at which CR and SS are both pronounced is a fundamental question of interest that is still elusive. We expect such a configuration to enable the brain to make synergistic and optimal use of these phenomena to process information efficiently. This paper considers a small-world network of excitable Hodgkin–Huxley neurons driven by channel noise and STDP with an asymmetric Hebbian time window. Numerical results indicate specific network topology and STDP parameter intervals in which CR and SS can be simultaneously enhanced. Our results imply that an optimally tuned inherent background noise, STDP rule, and network topology can play a constructive role in enhancing both the time precision of firing and the synchronization in neural systems. [ABSTRACT FROM AUTHOR]

Inspired by the nervous system working mechanism of a biological brain, brain-like intelligence has been a research frontier in the field of artificial intelligence. Under external stimulation, biological brains have self-adaptive advantages. Drawing from the advantage of biological brains, it is meaningful to investigate the anti-interference ability of brain-like models. In this study, we proposed a spiking neural network with small-world topology (SWSNN), where Izhikevich neuron models and synaptic plasticity models with excitatory and inhibitory synapses are introduced to represent nodes and edges of the network, respectively. The anti-interference ability of the SWSNN against pulse noise is investigated, and the anti-interference ability of SNNs with different topologies are compared. The simulation results indicate that: (i) our SWSNN has anti-interference ability against pulse noise, which is supported from different perspectives based on two indices. Furthermore, the chain reaction of firing rates, synaptic weights and topological characteristics forms neural information processing in the SWSNN under pulse noise. In addition, the synaptic weights are significantly relevant to the anti-interference ability, which implies that an intrinsic factor of the anti-interference ability is the dynamic regulation of synaptic plasticity. (ii) Our SWSNN outperforms the random and regular SNNs that are not complex networks in terms of anti-interference performance. For complex network, the anti-interference performance of SWSNN is superior to that of scale-free SNN, and the anti-interference superiority of the SWSNN is more obvious with the increase of amplitude of pulse noise. The topological characteristics of the network are further discussed, and the results imply that the topology is a factor that affects the anti-interference performance of the SWSNN. [ABSTRACT FROM AUTHOR]

Purpose: Brain health is a dynamic state involving cognitive, emotional, and motor domains. Measuring brain health is a challenge owing to the uncertainty as to whether it is one or many constructs. This study aimed to contribute evidence for brain health as a unified construct by estimating the strength of relationships between and among patient-reported items related to the brain health construct in a population with brain vulnerability owing to HIV.Data for this cross-sectional analysis came from a Canadian cohort of people aging with HIV. The sample included 710 men recruited between 2014 and 2016 from five Canadian cities. A network analysis was conducted with 30 items selected from the brain-related domains of fatigue, cognition, depression, sleep, anxiety, and motivation. Node centrality measures were used to determine the most critical items in the network.The network showed small-world properties, that is, most nodes can be reached from other nodes with few hops,” indicating strong connectivity. The most central symptoms were “How much do you enjoy life?” and “How often do you have negative feelings?”.The small-world properties of the network structure indicate that brain health items are interconnected and may be influenced by shared underlying factors. The centrality indices suggest that items related to enjoyment of life and negative feelings may be particularly important for understanding brain health in this population. Future research should aim to replicate these findings in larger and more diverse samples to confirm their robustness and generalizability.Methods: Brain health is a dynamic state involving cognitive, emotional, and motor domains. Measuring brain health is a challenge owing to the uncertainty as to whether it is one or many constructs. This study aimed to contribute evidence for brain health as a unified construct by estimating the strength of relationships between and among patient-reported items related to the brain health construct in a population with brain vulnerability owing to HIV.Data for this cross-sectional analysis came from a Canadian cohort of people aging with HIV. The sample included 710 men recruited between 2014 and 2016 from five Canadian cities. A network analysis was conducted with 30 items selected from the brain-related domains of fatigue, cognition, depression, sleep, anxiety, and motivation. Node centrality measures were used to determine the most critical items in the network.The network showed small-world properties, that is, most nodes can be reached from other nodes with few hops,” indicating strong connectivity. The most central symptoms were “How much do you enjoy life?” and “How often do you have negative feelings?”.The small-world properties of the network structure indicate that brain health items are interconnected and may be influenced by shared underlying factors. The centrality indices suggest that items related to enjoyment of life and negative feelings may be particularly important for understanding brain health in this population. Future research should aim to replicate these findings in larger and more diverse samples to confirm their robustness and generalizability.Results: Brain health is a dynamic state involving cognitive, emotional, and motor domains. Measuring brain health is a challenge owing to the uncertainty as to whether it is one or many constructs. This study aimed to contribute evidence for brain health as a unified construct by estimating the strength of relationships between and among patient-reported items related to the brain health construct in a population with brain vulnerability owing to HIV.Data for this cross-sectional analysis came from a Canadian cohort of people aging with HIV. The sample included 710 men recruited between 2014 and 2016 from five Canadian cities. A network analysis was conducted with 30 items selected from the brain-related domains of fatigue, cognition, depression, sleep, anxiety, and motivation. Node centrality measures were used to determine the most critical items in the network.The network showed small-world properties, that is, most nodes can be reached from other nodes with few hops,” indicating strong connectivity. The most central symptoms were “How much do you enjoy life?” and “How often do you have negative feelings?”.The small-world properties of the network structure indicate that brain health items are interconnected and may be influenced by shared underlying factors. The centrality indices suggest that items related to enjoyment of life and negative feelings may be particularly important for understanding brain health in this population. Future research should aim to replicate these findings in larger and more diverse samples to confirm their robustness and generalizability.Conclusion: Brain health is a dynamic state involving cognitive, emotional, and motor domains. Measuring brain health is a challenge owing to the uncertainty as to whether it is one or many constructs. This study aimed to contribute evidence for brain health as a unified construct by estimating the strength of relationships between and among patient-reported items related to the brain health construct in a population with brain vulnerability owing to HIV.Data for this cross-sectional analysis came from a Canadian cohort of people aging with HIV. The sample included 710 men recruited between 2014 and 2016 from five Canadian cities. A network analysis was conducted with 30 items selected from the brain-related domains of fatigue, cognition, depression, sleep, anxiety, and motivation. Node centrality measures were used to determine the most critical items in the network.The network showed small-world properties, that is, most nodes can be reached from other nodes with few hops,” indicating strong connectivity. The most central symptoms were “How much do you enjoy life?” and “How often do you have negative feelings?”.The small-world properties of the network structure indicate that brain health items are interconnected and may be influenced by shared underlying factors. The centrality indices suggest that items related to enjoyment of life and negative feelings may be particularly important for understanding brain health in this population. Future research should aim to replicate these findings in larger and more diverse samples to confirm their robustness and generalizability. [ABSTRACT FROM AUTHOR]

The external segment of globus pallidus (GPe) is a network of oscillatory neurons connected by inhibitory synapses. We studied the intrinsic dynamic and the response to a shared brief inhibitory stimulus in a model GPe network. Individual neurons were simulated using a phase resetting model based on measurements from mouse GPe neurons studied in slices. The neurons showed a broad heterogeneity in their firing rates and in the shapes and sizes of their phase resetting curves. Connectivity in the network was set to match experimental measurements. We generated statistically equivalent neuron heterogeneity in a small-world model, in which 99% of connections were made with near neighbors and 1% at random, and in a model with entirely random connectivity. In both networks, the resting activity was slowed and made more irregular by the local inhibition, but it did not show any periodic pattern. Cross-correlations among neuron pairs were limited to directly connected neurons. When stimulated by a shared inhibitory input, the individual neuron responses separated into two groups: one with a short and stereotyped period of inhibition followed by a transient increase in firing probability, and the other responding with a sustained inhibition. Despite differences in firing rate, the responses of the first group of neurons were of fixed duration and were synchronized across cells. [ABSTRACT FROM AUTHOR]

Simulation modelling has gained ground over the years since it can provide various scenarios applied to any scientific area. In this study, a stochastic cellular automata model is proposed, in which agents fall into three distinct categories (adopters, non-adopters and denials). Based on Hofstede's cultural dimension individualism, we characterize three major international markets, as perfectly clustered (collective) to perfectly random (individualistic). We investigate innovation diffusion speed, in each network topology. At each time step, the decision of non-adopters to purchase innovative products, is affected by their immediate neighborhood (von Neumman). The speed of diffusion is evaluated using time at which sales reach 50% of market. Effects of simulation parameters on speed of diffusion, are assessed using a log-normal accelerated failure time model. Results demonstrate that diffusion of innovative products accelerates when innovators of a virtual economic system are placed according to a random network and when amount of innovators and imitators in the economic system increases. Slower innovative products' diffusion process is a result of a large amount of denials and of how imitators are placed in the in the virtual economic system. Diffusion in small-world virtual economic systems lead to small time inflexion points very close to those of a random networked market. [ABSTRACT FROM AUTHOR]

Pandemic with mutation and permanent immune spreading in a small-world network described is studied by a modified SIR model, with consideration of mutation-immune mechanism. First, a novel mutation-immune model is proposed to modify the classical SIR model to simulate the transmission of mutable viruses that can be permanently immunized in small-world networks. Then, the influences of the size, coordination number and disorder parameter of the small-world network on the spread of the epidemic are analyzed in detail. Finally, the influences of mutation cycle and infection rate on epidemic transmission in small-world network are investigated further. The results show that the structure of the small-world network and the virus mutation cycle have an important impact on the spread of the epidemic. For viruses that can be permanently immunized, virus mutation is equivalent to making the immune cycle of human beings from infinite to finite. The dynamical behavior of the modified SIR epidemic model changes from an irregular, low-amplitude evolution at small disorder parameter to a spontaneous state of wide amplitude oscillations at large disorder parameter. Moreover, similar transition can also be found in increasing mutation cycle parameter. The maximum valid variation mutation decreases with the increase of disorder parameter and coordination number, but increase with respect to system size. In addition above, as the infection rate increases, the fraction of the infected increases and then decreases. As the mutation cycle increases, the time-average fraction of the infected and the infection rate corresponding to the maximum time-average fraction of the infected also decrease. As one conclusion, the results could give a deep understanding Pandemic with mutation and permanent immune spreading, from viewpoint of small-world network. [ABSTRACT FROM AUTHOR]

To simulate the price fluctuation dynamics of financial markets, a novel financial price model is developed by the voter dynamic system on the Watts-Strogtz small-world network and the random jump process. The voter system is a classical statistical physics system, which describes the dynamics of voters' attitudes towards a certain topic in the mutual influence. The Watts-Strogtz small-world network is a special kind of complex networks, which can be used to study the transmission dynamics of different things in complex and real-world systems. The paper first attempts to use the voter dynamic system on the small-world network to reproduce the micro-mechanism of price fluctuations caused by the interaction among different investors in financial markets, where investors can potentially disseminate information and interact via additional long-distance contacts. Moreover, considering that external macro environments have the impact on price fluctuations in financial markets, this paper introduces the random jump process in the price model. The effectiveness of the proposed model can be verified by comparing price returns generated by the model with returns of several important stock indexes in terms of nonlinear fluctuation behaviors. First, some statistical behaviors of the fluctuation dynamics are explored, including distribution characteristics and autocorrelation. Moreover, based on the ensemble empirical mode decomposition method, multifractal behaviors and complexity behaviors of returns and the first three intrinsic mode functions are investigated. The empirical results show that the dynamical model can well simulate these nonlinear fluctuation behaviors of real markets. [ABSTRACT FROM AUTHOR]

Based on the topological characteristics of small-world networks, a nonlinear sliding mode controller is designed to minimize the effects of internal parameter uncertainties. To qualify the effects of uncertain parameters in the response networks, some effective recognition rates are designed so as to achieve a steady value in the extremely fast simulation time period. Meanwhile, the Fisher-Kolmogorov and Burgers spatiotemporal chaotic systems are selected as the network nodes for constructing a drive and a response network, respectively. The simulation results confirm that the developed sliding mode could realize the effective synchronization problem between the spatiotemporal networks, and the outer synchronization is still achieved timely even when the connection probability of the small-world networks changes. [ABSTRACT FROM AUTHOR]

The scientific researches are focused on network topologies and training algorithms fields because they reduce overfitting problem in artificial neural networks. In this context, we showed in our previous work that Newman–Watts small-world feed-forward artificial neural networks present better classification and prediction performance than conventional feed-forward artificial neural networks. In this study, we investigate the effects of the Resilient back-propagation algorithm on SW network topology and propose a Resilient Newman–Watts small-world feed-forward artificial neural network model by assuming fixed initial topological conditions. We find that Resilient small-world network further reduces overfitting and further increases the network performance when compared to the conventional feed-forward artificial neural networks. Furthermore, it is shown that the proposed network model does not increase the algorithmic complexity as per other models. The obtained results imply that the proposed model can contribute to the solving of overfitting problem encountered in both deep neural networks and conventional artificial neural networks. [ABSTRACT FROM AUTHOR]

A common variant (rs53576, G/A) in the oxytocin receptor (OXTR) gene is associated with individual differences in social behavior and may increase the risk for neuropsychiatric disorders characterized by social impairment, especially autism. Although recent functional magnetic resonance imaging (fMRI) studies have identified functional connectivity alteration in some brain regions in risk A allele carriers, it is currently unknown whether this dysfunctional connectivity causes disruption of the topological properties of brain functional networks. We applied a graph-theoretical analysis to investigate the topological properties of brain networks derived from resting-state fMRI in relation to AA homozygotes versus G allele carriers in 290 cognitive normal young adults. We found both AA homozygotes and G allele carriers demonstrated small-world properties; however, male AA homozygotes showed lower normalized clustering coefficient, small-worldness, and local efficiency compared with male G allele carriers, no differences survived after Bonferroni correction; and the inter-group differences of all three metrics exhibited an allele-load-dependent trend (AA < AG < GG), indicating a randomization shift of their brain functional networks. No significant results were observed in any global measures in female AA homozygotes as compared to female G allele carriers. Our results suggested that the topological patterns of brain functional networks were altered in OXTR rs53576 male homozygotes for the risk A allele compared with male G allele carriers, providing evidence for the disruption of integrity in large-scale intrinsic brain networks in a sex-dimorphic manner. [ABSTRACT FROM AUTHOR]

The investigation of earthquake recurrence networks that were constructed from two aftershock sequences in Greece, is performed, aiming to detect whether the structure of networks became distinct (non-random) before the occurrence of either the main shock or a major (strong) aftershock. The network nodes are the time series observations, which are the aftershocks magnitudes. Their binary connections are given by the arbitrary threshold ε on the recurrence matrix, which is computed with the Heaviside function. Two aftershock sequences are the 2014 Kefalonia doublet, main shocks ( M = 6.1 and M = 6.0 ), and the 2015 Lefkada main shock ( M = 6.5 ) sequence. The earthquake networks are formed for three different thresholds of ε , for monitoring eight basic network measures and non-trivial properties such as the small world and scale free, and examining their structure during the evolution of the sequences. To assess whether the values of the eight network measures are statistically significant and the network gets non-trivial properties are present, the construction of randomized networks is required and then the comparison of the randomized network values with the ones from the original recurrence networks. The monitoring of network measures reveals that their original values diverge from the statistical significance, i.e., the structure of networks is random, immediately after the main shocks and shortly before the occurrence of the strongest aftershocks. On the contrary, in the intervening time, between the occurrences of main shocks and strongest aftershocks, their original values are statistically significant, which means that reveal the distinct network structure. The small-world and scale-free properties are sought but not revealed. [ABSTRACT FROM AUTHOR]

A network is scale-free if its connectivity density function is proportional to a power-law distribution. It has been suggested that scale-free networks may provide an explanation for the robustness observed in certain physical and biological phenomena, since the presence of a few highly connected hub nodes and a large number of small-degree nodes may provide alternate paths between any two nodes on average—such robustness has been suggested in studies of metabolic networks, gene interaction networks and protein folding. A theoretical justification for why many networks appear to be scale-free has been provided by Barabási and Albert, who argue that expanding networks, in which new nodes are preferentially attached to highly connected nodes, tend to be scale-free. In this paper, we provide the first efficient algorithm to compute the connectivity density function for the ensemble of all homopolymer secondary structures of a user-specified length—a highly nontrivial result, since the exponential size of such networks precludes their enumeration. Since existent power-law fitting software, such as powerlaw, cannot be used to determine a power-law fit for our exponentially large RNA connectivity data, we also implement efficient code to compute the maximum likelihood estimate for the power-law scaling factor and associated Kolmogorov–Smirnov p value. Hypothesis tests strongly indicate that homopolymer RNA secondary structure networks are not scale-free; moreover, this appears to be the case for real (non-homopolymer) RNA networks. [ABSTRACT FROM AUTHOR]

We formulate a model in which agents embedded in an exogenous social network decide whether to adopt a new network product or not. In the theoretical part of the paper, we characterize the stochastically stable equilibria for complete networks and cycles. For an arbitrary network structure, we develop a novel graph decomposition method to characterize the set of recurrent communication states, which is a superset of stochastically stable equilibria of the adoption game presented in our model. In the simulation part, we study the contagion process of a network product in small-world networks that systematically represent social networks. We simulate a generalization of the Morris (Rev Econ Stud 67(1):57–78, 2000) Contagion model that can explain the chasm between early adopters and early majority. Our numerical analysis shows that the failure of a new network product is less likely in a highly cliquish network. In addition, the contagion process reaches to steady state faster in random networks than in highly cliquish networks. It turns out that marketers should work with mixed marketing strategies, which will result in a full contagion of a network product and faster contagion rates with a higher probability. [ABSTRACT FROM AUTHOR]

Nowadays, the network data that we need to deal with and make sense of are becoming increasingly large and complex. Small-world networks are a type of complex networks whose underling graphs have small diameter, shorter average path length between nodes, and a high degree of clustering structures and can be found in a wide range of scientific fields, including social networks, sociology, computer science, business intelligence, and biology. However, conventional visualization algorithms for small-work networks lead to a uniform clump of nodes or are restricted to a tree structure, making the network structure difficult to identify and analyze. This work provides a new visual analytical method to improve the situation. Different from previous methods based on spanning trees, this method first generates a weighted planar sub-network based on the measurement of network centrality metrics. A force-directed algorithm based on node-edge repulsion is then applied to visualize this sub-network into a proper layout for better understanding of the data. Finally, the remaining links are placed back to maintain the original network's integrity. The experimental results show that compared to previous methods, the proposed method can be more effective in differentiating clusters and revealing relationship patterns among individual nodes and clusters in the network. Furthermore, the proposed method is applied to a data of the semiconductor wafer manufacturing industry as a case study. The work shows that this new approach allows users to gain useful insights into the data. [ABSTRACT FROM AUTHOR]

A higher risk of cognitive impairments has been found after an overt hepatic encephalopathy (OHE) episode in cirrhotic patients. We investigated the effect of prior OHE episodes on the topological organization of the functional brain network and its association with the relevant cognitive impairments. Resting-state functional MRI data were acquired from 41 cirrhotic patients (19 with prior OHE (Prior-OHE) and 22 without (Non-Prior-OHE)) and 21 healthy controls (HC). A Psychometric Hepatic Encephalopathy Score (PHES) assessed cognition. The whole-brain functional network was constructed by thresholding functional correlation matrices of 90 brain regions (derived from the Automated Anatomic Labeling atlas). The topological properties of the brain network, including small-worldness, network efficiency, and nodal efficiency, were examined using graph theory-based analysis. Globally, the Prior-OHE group had a significantly decreased clustering coefficient and local efficiency, compared with the controls. Locally, the nodal efficiency in the bilateral medial superior frontal gyrus and the right postcentral gyrus decreased in the Prior-OHE group, while the nodal efficiency in the bilateral anterior cingulate/paracingulate gyri and right superior parietal gyrus increased in the Prior-OHE group. The alterations of global and regional network parameters progressed from Non-Prior-OHE to Prior-OHE and the clustering coefficient and local efficiency values were significantly correlated with PHES results. In conclusion, cirrhosis leads to the reduction of brain functional network efficiency, which could be aggravated by a prior OHE episode. Aberrant topological organization of the functional brain network may contribute to a higher risk of cognitive impairments in Prior-OHE patients. [ABSTRACT FROM AUTHOR]

Neural information coding is the fundamental of information processing mechanism in biological neural network. The study of neural information coding can help to understand the function of information processing in biological neural network and lay the theoretical foundation for improving bionic ability. As the abstract of a large number of real complex systems, small-world networks have the properties of biological neural networks. However, the neural information coding based on the small-world topology is rarely studied and the information transmission mechanism among the neurons is mostly excitatory regulation mechanism of spike-timing-dependent plasticity (STDP). In this paper, the small-world network is constructed and its properties are analyzed; the small-world spiking neural network based on the more complete STDP including excitatory synapse and inhibitory synapse is constructed; from the angle of firing rate of neurons and the temporal structure of the spike train, the properties of information coding on the small-world spiking neural network under the stimulations of white Gauss noise and impulse noise are analyzed respectively. Our experimental results indicate that under the same stimulation, the responses of the mean rate coding and ISI coding of the small-world network are both enhanced with the increase of stimulation intensity; under different stimulations, the mean rate coding and ISI coding of the small-world network show respective specificity. [ABSTRACT FROM AUTHOR]

We study the effects of different coupling strengths and network topologies on signal detection in small-world neuronal networks. Research has previously revealed that the ability of detecting subthreshold signals could be significantly enhanced by appropriately fine-tuning the noise intensity. Here we show that the coupling strength and the structure of the underlying network can also lead toward enhanced signal detection. In particular, we show that there are two levels of the coupling strength at which the subthreshold signal can be detected at an appropriate noise intensity and network structure. We also show that the network structure has little impact on signal detection if the coupling is weak. On the other hand, for intermediate coupling strengths, we show that the shorter the average path length, the better the signal detection. Finally, if the coupling is strong, we show that there exists an intermediate average path length at which signal detection becomes optimal. [ABSTRACT FROM AUTHOR]

In this study, we aimed to investigate the functional network changes that occur in patients with lower back pain (LBP). We also investigated the link between LBP and the small-world properties of functional networks within the brain. Functional MRI (fMRI) was performed on 20 individuals with LBP and 17 age and gender-matched normal controls during the resting state. The severity of the pain in the individuals with LBP ranged from 5 to 8 on a 0-10 scale, with 0 indicating no pain. Network-based statistics were performed to investigate the differences between the brain networks of individuals with LBP and those of normal controls. Several small-world parameters of brain networks were calculated, including the clustering coefficient, characteristic path length, local efficiency, and global efficiency. These criteria reflect the overall network efficiency. The brain networks in the individuals with LBP due to herniation of a lumbar disc demonstrated a significantly longer characteristic path length as well as a lower clustering coefficient, global efficiency, and local efficiency compared to those in control subjects. We found that LBP patients tended to have unstable and inefficient brain networks when compared with healthy controls. In addition, LBP individuals showed significantly decreased functional connectivity in the anterior cingulate cortex, middle cingulate cortex, post cingulate cortex, inferior frontal gyrus, middle temporal gyrus, occipital gyrus, postcentral gyrus, precentral gyrus, supplementary motor area, thalamus, fusiform, caudate, and cerebellum. We believe that these regions may be involved in the pathophysiology of lower back pain. [ABSTRACT FROM AUTHOR]

We consider the Maki-Thompson model for the stochastic propagation of a rumour within a population. In this model the population is made up of 'spreaders', 'ignorants' and 'stiflers'; any spreader attempts to pass the rumour to the other individuals via pair-wise interactions and in case the other individual is an ignorant, it becomes a spreader, while in the other two cases the initiating spreader turns into a stifler. In a finite population the process will eventually reach an equilibrium situation where individuals are either stiflers or ignorants. We extend the original hypothesis of homogenously mixed population by allowing for a small-world network embedding the model, in such a way that interactions occur only between nearest-neighbours. This structure is realized starting from a k-regular ring and by inserting, in the average, c additional links in such a way that k and c are tuneable parameters for the population architecture. We prove that this system exhibits a transition between regimes of localization (where the final number of stiflers is at most logarithmic in the population size) and propagation (where the final number of stiflers grows algebraically with the population size) at a finite value of the network parameter c. A quantitative estimate for the critical value of c is obtained via extensive numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a class of models for generating spatial versions of three classic networks: Erdös-Rényi (ER), Watts-Strogatz (WS), and Barabási-Albert (BA). We assume that nodes have geographical coordinates, are uniformly distributed over an m × m Cartesian space, and long-distance connections are penalized. Our computational results show higher clustering coefficient, assortativity, and transitivity in all three spatial networks, and imperfect power law degree distribution in the BA network. Furthermore, we analyze a special case with geographically clustered coordinates, resembling real human communities, in which points are clustered over k centers. Comparison between the uniformly and geographically clustered versions of the proposed spatial networks show an increase in values of the clustering coefficient, assortativity, and transitivity, and a lognormal degree distribution for spatially clustered ER, taller degree distribution and higher average path length for spatially clustered WS, and higher clustering coefficient and transitivity for the spatially clustered BA networks. [ABSTRACT FROM AUTHOR]

In recent years, China's urbanization has developed very quickly. Many scholars have conducted China's urbanization research (CUR) and have published a large number of articles. With CUR as a case, we construct the dynamic co-word network to analyze the characteristics of development about the knowledge system (KS). We draw several conclusions from this research. (1) The development of CUR possesses small-world characteristics and scale-free effects. The co-word network of CUR increases significantly to a large-scale network. (2) Betweenness centrality and eigenvector centrality of the dynamic co-word networks positively correlate with node degree. The popular nodes connecting with a large number of topics are also the nodes that occur in the critical paths. The popular keywords in CUR also bridge distant clusters of the related topics. (3) The clustering coefficients indicate that a number of topics with low degrees tend to relate to the adjacent topics more directly to form 'conglobation' clusters. The network is a hierarchical clustered structure. The hub keywords play a crucial role in bridging distinct clusters of highly associated keywords and make them form an integrated network. (4) Since 2003, the CUR has begun to develop systematically. From 1998 to 2015, the hotspots in CUR varied diversely, which are highly correlated with social issues and public concerns. (5) We proposed several practical implications on the development of CUR from the dynamic co-word network measures. Beyond the case of CUR's KS, we hope the versatility of methods in this research also provides enlightenment for other KS studies. [ABSTRACT FROM AUTHOR]

Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the Hopf bifurcation control of a small-world network model with time-delay. With emphasis on the relationship between the Hopf bifurcation and the time-delay, we investigate the effect of time-delay by choosing it as the bifurcation parameter. By using tools from control and bifurcation theory, it is proved that there exists a critical value of time-delay for the stability of the model. When the time-delay passes through the critical value, the model loses its stability and a Hopf bifurcation occurs. To enhance the stability of the model, we propose an improved hybrid control strategy in which state feedback and parameter perturbation are used. Through linear stability analysis, we show that by adjusting the control parameter properly, the onset of Hopf bifurcation of the controlled model can be delayed or eliminated without changing the equilibrium point of the model. Finally, numerical simulations are given to verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

Echo state networks (ESNs) with very simple and linear learning algorithm are a new approach to recurrent neural network training. Recently, these networks have aroused a lot of interest in their nonlinear dynamic system modeling capacities. In previous studies, the largest eigenvalue of the reservoir connectivity matrix (spectral radius) is used as a predictor for the stable network dynamics, but recent evidences show that in the presence of reservoir substructures like clusters, stability criteria in these kind of networks are altered. Some researchers have also demonstrated that network approximation ability in ESN networks is improved by the characteristics of small-world and scale-free. In this paper, we used three classic clustering algorithms called K-Means (C- Centeriod), partitioning Around Medoids (PAM) and ward algorithm, for clustering the internal neurons. After that, we refer to mean nodes in each cluster as backbone units and refer to the other neurons in a cluster as local neurons. Connections between neurons are such that the resulting networks have small-world topology and neurons in the new networks follow a power-law distribution. At first, we demonstrate that resulting clustered networks have some characteristics of biological neural system like power-law distribution, small-word feature, community structure, and distributed architecture. For investigating the prediction power and the range of spectral radius of resulting networks, we use new ESNs on the Mackey-Glass dynamic system and the laser time series prediction problem and compared the results with the previous works. Then we evaluate echo state property and performance of approximating highly complex nonlinear dynamic systems of proposed networks rather than previous approaches. Results show that the proposed methods outperform the previous ones in terms of prediction accuracy of chaotic time series. [ABSTRACT FROM AUTHOR]

A wide range of evidence has shown that information encoding performed by the visual cortex involves complex activities of neuronal populations. However, the effects of the neuronal connectivity structure on the population's encoding performance remain poorly understood. In this paper, a small-world-based population encoding model of the primary visual cortex (V1) is established on the basis of the generalized linear model (GLM) to describe the computation of the neuronal population. The model mainly consists of three sets of filters, including a spatiotemporal stimulus filter, a post-spike history filter, and a set of coupled filters with the coupling neurons organizing as a small-world network. The parameters of the model were fitted with neuronal data of the rat V1 recorded with a micro-electrode array. Compared to the traditional GLM, without considering the small-world structure of the neuronal population, the proposed model was proved to produce more accurate spiking response to grating stimuli and enhance the capability of the neuronal population to carry information. The comparison results proved the validity of the proposed model and further suggest the role of small-world structure in the encoding performance of local populations in V1, which provides new insights for understanding encoding mechanisms of a small scale population in visual system. [ABSTRACT FROM AUTHOR]

The outer synchronization of irregular coupled complex networks is investigated with nonidentical topological structures. The switching gain is estimated by an adaptive technique, and a sliding mode controller is designed to satisfy the sliding condition. The outer synchronization between two irregular coupled complex networks with different initial conditions is implemented via the designed controllers with the corresponding parameter update laws. The chaos synchronization of two small-world networks consisting of N uncertain identical Lorenz systems is achieved to demonstrate the applications of the proposed approach. [ABSTRACT FROM AUTHOR]

For decades, the spatial approach to network analysis has principally focused on planar and technical networks from a classic graph theory perspective. Reference to models and methods developed by other disciplines on non-planar networks, such as sociology and physics, is recent, limited, and dispersed. Conversely, the physics literature that developed the popular scale-free and small-world models pays an increasing attention to the spatial dimension of networks. Reviewing how complex network research has been integrated into geography and regional science reveals a high heterogeneity among spatial scientists as well as key directions for increasing their role inside multidisciplinary researches on networks. [ABSTRACT FROM AUTHOR]

In this paper, the oscillations and synchronization status of two different network connectivity patterns based on Izhikevich model are studied. One of the connectivity patterns is a randomly connected neuronal network, the other one is a small-world neuronal network. This Izhikevich model is a simple model which can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Detailed investigations reveal that by varying some key parameters, such as the connection weights of neurons, the external current injection, the noise of intensity and the neuron number, this neuronal network will exhibit various collective behaviors in randomly coupled neuronal network. In addition, we show that by changing the number of nearest neighbor and connection probability in small-world topology can also affect the collective dynamics of neuronal activity. These results may be instructive in understanding the collective dynamics of mammalian cortex. [ABSTRACT FROM AUTHOR]

In reality, manufacturers motivate sales agents to increase promotional effort with the sales rebate and penalty contract, the agents always concern about the fairness, and information flow structure among agents is a small-world network. In order to improve incentive performance under this situation, this paper builds a multi-agent model mainly to observe the impact of small-world network characteristics on incentive effects. Experimental results show that greater probability of replacement comparison objects will induce lower profit and products sales volume of the manufacturer; agents will have a huge negative impact on incentive effects when they get related information from a very small amount of agents. The aim of this paper is to provide suggestions for manufacturers to utilize the characteristic of small-world network rational so as to improve the incentive effect. [ABSTRACT FROM AUTHOR]

Phase transition of a protein globule is considered in the frameworks of (i) the generalized mean-field theory for the order parameter, characterizing the extent of the deviation of a protein three-dimensional structure from its native state and (ii) the network model that treats a protein globule as a small-world network with a significant percent of long-range links between amino acid residues. Temperature dependencies of the introduced order parameter are defined and phase-transition temperatures are found on the basis of the function defining the distribution of links' numbers for protein residues. An important role of long-range links, promoting considerable rise of thermal protein stability, is demonstrated by the example of a correlation between protein melting temperature and a fraction of disulfide bonds. [ABSTRACT FROM AUTHOR]

The understanding of scientific knowledge itself may promote further advances in science and research on the organization of knowledge may be an initiative to this effort. This stream of research, however, has been mainly driven by the analysis of citation networks. This study uses, as an alternative knowledge element, information on the keywords of papers published in business research and examines how they are associated with each other to constitute a body of scientific knowledge. The results show that, unlike most citation networks, keyword networks are not small-word networks but, rather, locally clustered scale-free networks with a hierarchic structure. These structural patterns are robust against the scope of scientific fields involved. In addition, this paper discusses the origins and implications of the identified structural characteristics of keyword networks. [ABSTRACT FROM AUTHOR]

The topology of the trust network is important to optimize its usage in the trust-aware applications. However, since the users can join trust network ubiquitously, the structure of the highly dynamic trust network is still unknown. This paper contributes to verify that the trust network is the small-world network, and its small-world topology is independent of its dynamics. This is achieved by verifying the scale-freeness of five trust networks extracted from real online sites. Using the small-world nature of the trust network, we optimize the rating prediction mechanism of the conventional trust-aware recommender system. Experimental results clearly show that our proposed mechanism can achieve the maximum accuracy and coverage with the minimum computation complexity for the rating predictions. [ABSTRACT FROM AUTHOR]

Inspired by the temporal correlation theory of brain functions, researchers have presented a number of neural oscillator networks to implement visual scene segmentation problems. Recently, it is shown that many biological neural networks are typical small-world networks. In this paper, we propose and investigate two small-world models derived from the well-known LEGION (locally excitatory and globally inhibitory oscillator network) model. To form a small-world network, we add a proper proportion of unidirectional shortcuts (random long-range connections) to the original LEGION model. With local connections and shortcuts, the neural oscillators can not only communicate with neighbors but also exchange phase information with remote partners. Model 1 introduces excitatory shortcuts to enhance the synchronization within an oscillator group representing the same object. Model 2 goes further to replace the global inhibitor with a sparse set of inhibitory shortcuts. Simulation results indicate that the proposed small-world models could achieve synchronization faster than the original LEGION model and are more likely to bind disconnected image regions belonging together. In addition, we argue that these two models are more biologically plausible. [ABSTRACT FROM AUTHOR]

Following a nonlinear dynamics approach, we investigated the emergence of functional clusters which are related with spontaneous brain activity during sleep. Based on multichannel EEG traces from 10 healthy subjects, we compared the functional connectivity across different sleep stages. Our exploration commences with the conjecture of a small-world patterning, present in the scalp topography of the measured electrical activity. The existence of such a communication pattern is first confirmed for our data and then precisely determined by means of two distinct measures of non-linear interdependence between time-series. A graph encapsulating the small-world network structure along with the relative interdependence strength is formed for each sleep stage and subsequently fed to a suitable clustering procedure. Finally the delineated graph components are comparatively presented for all stages revealing novel attributes of sleep architecture. Our results suggest a pivotal role for the functional coupling during the different stages and indicate interesting dynamic characteristics like its variable hemispheric asymmetry and the isolation between anterior and posterior cortical areas during REM. [ABSTRACT FROM AUTHOR]

By using the tools of statistical physics and recent investigations of the scaling properties of different complex networks, the structural and evolving properties of the Chinese railway network (CRN) is studied. It has been verified that the CRN has the same small-world properties of the Indian railway network (IRN). According to the class of small-world networks, we believe the CRN is a single scale. In addition, a novel result is obtained. Measurements on the CRN indicate that the rate at which nodes acquire links depends on the node’s degree and follows a power law. [ABSTRACT FROM AUTHOR]

Studies on small-world networks have received intensive interdisciplinary attention during the past several years. It is well-known among researchers that a small-world network is often characterized by high connectivity and clustering, but so far there exist few effective approaches to evaluate small-world properties, especially for spatial networks. This paper proposes a method to examine the small-world properties of spatial networks from the perspective of network autocorrelation. Two network autocorrelation statistics, Moran’s I and Getis–Ord’s G, are used to monitor the structural properties of networks in a process of “rewiring” networks from a regular to a random network. We discovered that Moran’s I and Getis–Ord’s G tend to converge and have relatively low values when properties of small-world networks emerge. Three transportation networks at the national, metropolitan, and intra-city levels are analyzed using this approach. It is found that spatial networks at these three scales possess small-world properties when the correlation lag distances reach certain thresholds, implying that the manifestation of small-world phenomena result from the interplay between the network structure and the dynamics taking place on the network. [ABSTRACT FROM AUTHOR]