Showing papers in "Physica A-statistical Mechanics and Its Applications in 2010"
TL;DR: Looking at 33 metro systems in the world, network science methodologies are adapted to the transportation literature, and one application to the robustness of metros is offered; here, metro refers to urban rail transit with exclusive right-of-way, whether it is underground, at grade or elevated.
Abstract: Transportation systems, being real-life examples of networks, are particularly interesting to analyze from the viewpoint of the new and rapidly emerging field of network science. Two particular concepts seem to be particularly relevant: scale-free patterns and small-worlds. By looking at 33 metro systems in the world, this paper adapts network science methodologies to the transportation literature, and offers one application to the robustness of metros; here, metro refers to urban rail transit with exclusive right-of-way, whether it is underground, at grade or elevated. We find that most metros are indeed scale-free (with scaling factors ranging from 2.10 to 5.52) and small-worlds; they show atypical behaviors, however, with increasing size. In particular, the presence of transfer-hubs (stations hosting more than three lines) results in relatively large scaling factors. The analysis provides insights/recommendations for increasing the robustness of metro networks. Smaller networks should focus on creating transfer stations, thus generating cycles to offer alternative routes. For larger networks, few stations seem to detain a certain monopole on transferring, it is therefore important to create additional transfers, possibly at the periphery of city centers; the Tokyo system seems to remarkably incorporate these properties.
351 citations
TL;DR: In this paper, a Voronoi diagram is used to assign a personal space to every pedestrian to reduce the density scatter and calculate direction and speed from position differences between times with identical phases of movement.
Abstract: The progress of image processing during recent years allows the measurement of pedestrian characteristics on a “microscopic” scale with low costs. However, density and flow are concepts of fluid mechanics defined for the limit of infinitely many particles. Standard methods of measuring these quantities locally (e.g. counting heads within a rectangle) suffer from large data scatter. The remedy of averaging over large spaces or long times reduces the possible resolution and inhibits the gain obtained by the new technologies. In this contribution we introduce a concept for measuring microscopic characteristics on the basis of pedestrian trajectories. Assigning a personal space to every pedestrian via a Voronoi diagram reduces the density scatter. Similarly, calculating direction and speed from position differences between times with identical phases of movement gives low-scatter sequences for speed and direction. Finally we discuss the methods to obtain reliable values for derived quantities and new possibilities of an in-depth analysis of experiments. The resolution obtained indicates the limits of stationary state theory.
332 citations
TL;DR: In this article, a synthesis of evolutionary game theory, nonlinear dynamics, and the theory of stochastic processes is presented for a deeper understanding of these ecological systems, and a general concept for defining survival and extinction on ecological time-scales.
Abstract: Ecological systems are complex assemblies of large numbers of individuals, interacting competitively under multifaceted environmental conditions. Recent studies using microbial laboratory communities have revealed some of the self-organization principles underneath the complexity of these systems. A major role of the inherent stochasticity of its dynamics and the spatial segregation of different interacting species into distinct patterns has thereby been established. It ensures the viability of microbial colonies by allowing for species diversity, cooperative behavior and other kinds of “social” behavior.
A synthesis of evolutionary game theory, nonlinear dynamics, and the theory of stochastic processes provides the mathematical tools and a conceptual framework for a deeper understanding of these ecological systems. We give an introduction into the modern formulation of these theories and illustrate their effectiveness focussing on selected examples of microbial systems. Intrinsic fluctuations, stemming from the discreteness of individuals, are ubiquitous, and can have an important impact on the stability of ecosystems. In the absence of speciation, extinction of species is unavoidable. It may, however, take very long times. We provide a general concept for defining survival and extinction on ecological time-scales. Spatial degrees of freedom come with a certain mobility of individuals. When the latter is sufficiently high, bacterial community structures can be understood through mapping individual-based models, in a continuum approach, onto stochastic partial differential equations. These allow progress using methods of nonlinear dynamics such as bifurcation analysis and invariant manifolds. We conclude with a perspective on the current challenges in quantifying bacterial pattern formation, and how this might have an impact on fundamental research in non-equilibrium physics.
242 citations
TL;DR: Experiments show that LPAm+ successfully detects communities with higher modularity values than ever reported in two commonly used real-world networks and offers a fair compromise between accuracy and speed.
Abstract: A modularity-specialized label propagation algorithm (LPAm) for detecting network communities was recently proposed. This promising algorithm offers some desirable qualities. However, LPAm favors community divisions where all communities are similar in total degree and thus it is prone to get stuck in poor local maxima in the modularity space. To escape local maxima, we employ a multistep greedy agglomerative algorithm (MSG) that can merge multiple pairs of communities at a time. Combining LPAm and MSG, we propose an advanced modularity-specialized label propagation algorithm (LPAm+). Experiments show that LPAm+ successfully detects communities with higher modularity values than ever reported in two commonly used real-world networks. Moreover, LPAm+ offers a fair compromise between accuracy and speed.
233 citations
TL;DR: Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.
Abstract: Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this article, we propose a recommendation algorithm based on an integrated diffusion on user–item–tag tripartite graphs. We use three benchmark data sets, Del.icio.us , MovieLens and BibSonomy , to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.
231 citations
TL;DR: In this paper, the sampling properties of the Hurst exponent methods of estimation change with the presence of heavy tails and they run extensive Monte Carlo simulations to find out how rescaled range analysis (R/S), multifractal detrended fluctuation analysis (M F - D F A ), detrending moving average (D M A ) and generalized Hurst approach (G H E ) estimate Hurst exponents on independent series with different heavy tails.
Abstract: In this paper, we show how the sampling properties of the Hurst exponent methods of estimation change with the presence of heavy tails. We run extensive Monte Carlo simulations to find out how rescaled range analysis ( R / S ), multifractal detrended fluctuation analysis ( M F - D F A ), detrending moving average ( D M A ) and generalized Hurst exponent approach ( G H E ) estimate Hurst exponent on independent series with different heavy tails. For this purpose, we generate independent random series from stable distribution with stability exponent α changing from 1.1 (heaviest tails) to 2 (Gaussian normal distribution) and we estimate the Hurst exponent using the different methods. R / S and G H E prove to be robust to heavy tails in the underlying process. G H E provides the lowest variance and bias in comparison to the other methods regardless the presence of heavy tails in data and sample size. Utilizing this result, we apply a novel approach of the intraday time-dependent Hurst exponent and we estimate the Hurst exponent on high frequency data for each trading day separately. We obtain Hurst exponents for S&P500 index for the period beginning with year 1983 and ending by November 2009 and we discuss the surprising result which uncovers how the market’s behavior changed over this long period.
225 citations
TL;DR: In this article, the authors explore the network topology of the federal funds market and find that the network is sparse, exhibits the small-world phenomenon, and is disassortative.
Abstract: We explore the network topology of the federal funds market. This market is important for distributing liquidity throughout the financial system and for the implementation of monetary policy. The recent turmoil in global financial markets underscores its importance. We find that the network is sparse, exhibits the small-world phenomenon, and is disassortative. Centrality measures are useful predictors of the interest rate of a loan.
214 citations
TL;DR: The designed control scheme is robust against the system’s uncertainty and guarantees the property of asymptotical stability in the presence of an external disturbance.
Abstract: This paper deals with designing a sliding mode controller (SMC) for a fractional-order chaotic financial system. Using the sliding mode control technique, a sliding surface is determined. The sliding mode control law is derived to make the states of the fractional-order financial system asymptotically stable. The designed control scheme is robust against the system’s uncertainty and guarantees the property of asymptotical stability in the presence of an external disturbance. An illustrative simulation result is given to demonstrate the effectiveness of the proposed sliding mode control design.
197 citations
TL;DR: In this paper, the authors review simple aspects of the thermodynamic and dynamical properties of systems with long-range pairwise interactions (LRI), which decay as 1 / r d + σ at large distances r in d dimensions.
Abstract: We review simple aspects of the thermodynamic and dynamical properties of systems with long-range pairwise interactions (LRI), which decay as 1 / r d + σ at large distances r in d dimensions. Two broad classes of such systems are discussed. (i) Systems with a slow decay of the interactions, termed “strong” LRI, where the energy is super-extensive. These systems are characterized by unusual properties such as inequivalence of ensembles, negative specific heat, slow decay of correlations, anomalous diffusion and ergodicity breaking. (ii) Systems with faster decay of the interaction potential, where the energy is additive, thus resulting in less dramatic effects. These interactions affect the thermodynamic behavior of systems near phase transitions, where long-range correlations are naturally present. Long-range correlations are often present in systems driven out of equilibrium when the dynamics involves conserved quantities. Steady state properties of driven systems with local dynamics are considered within the framework outlined above.
194 citations
Spanish National Research Council1, National Scientific and Technical Research Council2, National University of La Plata3, Autonomous University of Madrid4, Universidade Católica de Brasília5, Central Bank of Brazil6, Pontifical Catholic University of Valparaíso7, Universidade Federal de Minas Gerais8, Facultad de Ciencias Exactas y Naturales9
TL;DR: This work proposes to use the complexity-entropy causality plane, a powerful tool for discriminating Gaussian from non-Gaussian process and different degrees of correlations, to distinguish the stage of stock market development.
Abstract: The complexity-entropy causality plane has been recently introduced as a powerful tool for discriminating Gaussian from non-Gaussian process and different degrees of correlations [O.A. Rosso, H.A. Larrondo, M.T. Martin, A. Plastino, M.A. Fuentes, Distinguishing noise from chaos, Phys. Rev. Lett. 99 (2007) 154102]. We propose to use this representation space to distinguish the stage of stock market development. Our empirical results demonstrate that this statistical physics approach is useful, allowing a more refined classification of stock market dynamics.
189 citations
TL;DR: A complex weighted network analysis of travel routes on the Singapore rail and bus transportation systems using both topological and dynamical analyses provides additional evidence that a dynamical study adds to the information gained by traditional topological analysis, providing a richer view of complex weighted networks.
Abstract: a b s t r a c t The structure and properties of public transportation networks have great implications for urban planning, public policies and infectious disease control. We contribute a complex weighted network analysis of travel routes on the Singapore rail and bus transportation systems. We study the two networks using both topological and dynamical analyses. Our results provide additional evidence that a dynamical study adds to the information gained by traditional topological analysis, providing a richer view of complex weighted networks. For example, while initial topological measures showed that the rail network is almost fully connected, dynamical measures highlighted hub nodes that experience disproportionately large traffic. The dynamical assortativity of the bus networks also differed from its topological counterpart. In addition, inspection of the weighted eigenvector centralities highlighted a significant difference in traffic flows for both networks during weekdays and weekends, suggesting the importance of adding a temporal perspective missing from many previous studies.
TL;DR: It is found that although the topology of CAN has remained steady during the past few years, there are many dynamic switchings inside the network, which have changed the relative importance of airports and airlines.
Abstract: With the rapid development of the economy and the accelerated globalization process, the aviation industry plays a more and more critical role in today’s world, in both developed and developing countries. As the infrastructure of aviation industry, the airport network is one of the most important indicators of economic growth. In this paper, we investigate the evolution of the Chinese airport network (CAN) via complex network theory. It is found that although the topology of CAN has remained steady during the past few years, there are many dynamic switchings inside the network, which have changed the relative importance of airports and airlines. Moreover, we investigate the evolution of traffic flow (passengers and cargoes) on CAN. It is found that the traffic continues to grow in an exponential form and has evident seasonal fluctuations. We also found that cargo traffic and passenger traffic are positively related but the correlations are quite different for different kinds of cities.
TL;DR: Wang et al. as discussed by the authors investigated the cross-correlations between Chinese A-share and B-share markets, and they found that the crosscorrelations were strongly multifractal in the short-term and weakly multifractor in the long-term.
Abstract: In this paper, we investigate the cross-correlations between Chinese A-share and B-share markets. Qualitatively, we find that the return series of Chinese A-share and B-share markets were overall significantly cross-correlated based on the analysis of a statistic. Quantitatively, employing the detrended cross-correlation analysis, we find that the cross-correlations were strongly multifractal in the short-term and weakly multifractal in the long-term. Moreover, the cross-correlations of small fluctuations were persistent and those of large fluctuations were anti-persistent in the short-term while cross-correlations of all kinds of fluctuations were persistent in the long-term. Using the method of rolling windows, we find that the cross-correlations were weaker and weaker over time, especially after the price-limited reform. We attribute the fact to the improvement of market efficiency. On the volatility series, our results show that the cross-correlations were much stronger than those between return series. Results from rolling windows show that the short-term cross-correlations between volatility series are still high now. We also provide some relevant discussions later.
TL;DR: A fast and efficient method for detecting community structure is proposed, which discovers the community structure by iteratively incorporating the community containing a node with the communities that contain the nodes with maximum similarity to this node to form a new community.
Abstract: The detection of the community structure in networks is beneficial to understand the network structure and to analyze the network properties. Based on node similarity, a fast and efficient method for detecting community structure is proposed, which discovers the community structure by iteratively incorporating the community containing a node with the communities that contain the nodes with maximum similarity to this node to form a new community. The presented method has low computational complexity because of requiring only the local information of the network, and it does not need any prior knowledge about the communities and its detection results are robust on the selection of the initial node. Some real-world and computer-generated networks are used to evaluate the performance of the presented method. The simulation results demonstrate that this method is efficient to detect community structure in complex networks, and the ZLZ metrics used in the proposed method is the most suitable one among local indices in community detection.
TL;DR: It is shown that an appropriate small-world topology can always restore synchronized activity if only the information transmission delays are short or moderate at most, whereas long delays can further detriment synchronization due to a dynamic clustering anti-phase synchronization transition.
Abstract: We study synchronization transitions and pattern formation on small-world networks consisting of Morris–Lecar excitable neurons in dependence on the information transmission delay and the rewiring probability. In addition, networks formed via gap junctional connections and coupling via chemical synapses are considered separately. For gap-junctionally coupled networks we show that short delays can induce zigzag fronts of excitations, whereas long delays can further detriment synchronization due to a dynamic clustering anti-phase synchronization transition. For the synaptically coupled networks, on the other hand, we find that the clustering anti-phase synchronization can appear as a direct consequence of the prolongation of information transmission delay, without being accompanied by zigzag excitatory fronts. Irrespective of the coupling type, however, we show that an appropriate small-world topology can always restore synchronized activity if only the information transmission delays are short or moderate at most. Long information transmission delays always evoke anti-phase synchronization and clustering, in which case the fine-tuning of the network topology fails to restore the synchronization of neuronal activity.
TL;DR: In this article, the authors investigated the topological properties of the Brazilian stock market networks and found that the relative importance of different sectors within the network varies and that stocks tend to cluster by sector.
Abstract: This paper investigates the topological properties of the Brazilian stock market networks. We build the minimum spanning tree, which is based on the concept of ultrametricity, using the correlation matrix for a variety of stocks of different sectors. Our results suggest that stocks tend to cluster by sector. We employ a dynamic approach using complex network measures and find that the relative importance of different sectors within the network varies. The financial, energy and material sectors are the most important within the network.
TL;DR: A hybrid approach for structural vulnerability analysis of power transmission networks is proposed, in which a DC power flow model with hidden failures is embedded into the traditional error and attack tolerance methodology to form a new scheme for power grids vulnerability assessment and modeling.
Abstract: Power grids have been studied as a typical example of real-world complex networks Different from previous methods, this paper proposes a hybrid approach for structural vulnerability analysis of power transmission networks, in which a DC power flow model with hidden failures is embedded into the traditional error and attack tolerance methodology to form a new scheme for power grids vulnerability assessment and modeling The new approach embodies some important characteristics of power transmission networks Furthermore, the simulation on the standard IEEE 118 bus system demonstrates that a critical region might exist and when the power grid operates in the region, it is vulnerable to both random and intentional attacks Finally, a brief theoretical analysis is presented to explain the new phenomena
TL;DR: In this article, the authors study the statistical properties of complex networks constructed from time series of energy dissipation rates in three-dimensional fully developed turbulence using the visibility algorithm and find that the skeleton of the visibility network exhibits excellent allometric scaling with the scaling exponent η = 1.163 ± 0.005.
Abstract: We study the statistical properties of complex networks constructed from time series of energy dissipation rates in three-dimensional fully developed turbulence using the visibility algorithm. The degree distribution is found to have a power-law tail with the tail exponent α = 3.0 . The exponential relation between the number of the boxes N B and the box size l B based on the edge-covering box-counting method illustrates that the network is not self-similar, which is also confirmed by the hub-hub attraction according to the visibility algorithm. In addition, it is found that the skeleton of the visibility network exhibits excellent allometric scaling with the scaling exponent η = 1.163 ± 0.005 .
TL;DR: An overlapping communities detecting algorithm is proposed whose main strategies are finding an initial partial community from a node with maximal node strength and adding tight nodes to expand the partial community.
Abstract: Identification of communities is significant in understanding the structures and functions of networks. Since some nodes naturally belong to several communities, the study of overlapping communities has attracted increasing attention recently, and many algorithms have been designed to detect overlapping communities. In this paper, an overlapping communities detecting algorithm is proposed whose main strategies are finding an initial partial community from a node with maximal node strength and adding tight nodes to expand the partial community. Seven real-world complex networks and one synthetic network are used to evaluate the algorithm. Experimental results demonstrate that the algorithm proposed is efficient for detecting overlapping communities in weighted networks.
TL;DR: It is found that pedestrians tend to select the exit with shorter distance to them, especially when the people density is small or medium, which reflects the fact that a crowd of people may not be rational to optimize the usage of multi-exits, especially in an emergency.
Abstract: The evacuation process in a teaching building with two neighboring exits is investigated by means of experiment and modeling. The basic parameters such as flow, density and velocity of pedestrians in the exit area are measured. The exit-selecting phenomenon in the experiment is analyzed, and it is found that pedestrians prefer selecting the closer exit even though the other exit is only a little far. In order to understand the phenomenon, we reproduce the experiment process with a modified biased random walk model, in which the preference of closer exit is achieved using the drift direction and the drift force. Our simulation results afford a calibrated value of the drift force, especially when it is 0.56, there is good agreement between the simulation results and the experimental results on the number of pedestrians selecting the closer exit, the average velocity through the exits, the cumulative distribution of the instantaneous velocity and the fundamental diagram of the flow through exits. According to the further simulation results, it is found that pedestrians tend to select the exit with shorter distance to them, especially when the people density is small or medium. But if the density is large enough, the flow rates of the two exits will become comparable because of the detour behaviors. It reflects the fact that a crowd of people may not be rational to optimize the usage of multi-exits, especially in an emergency.
TL;DR: It is found that the proposed model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion and the lateral separation effects greatly enhance the realism of car following models.
Abstract: In order to describe car following behavior in real world, this paper presents a non-lane-based car following model by incorporating the effects of the lane width in traffic. The stability condition of the model is obtained by using the linear stability theory. And numerical simulation is carried out to validate the analytic results. The property of the model is investigated, and it is found that the proposed model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion. The results implied that incorporating the lane width effects in car following model not only stabilize traffic flow and suppress the traffic jam, but also lower critical headway and increase capacity. Thus, the lateral separation effects greatly enhance the realism of car following models.
TL;DR: In this article, the authors used the Detrended Cross-Correlation Analysis (DCCA) to investigate the influence of sun activity represented by sunspot numbers on one of the climate indicators, specifically rivers, represented by river flow fluctuation for Daugava, Holston, Nolichucky and French Broad rivers.
Abstract: We use the Detrended Cross-Correlation Analysis (DCCA) to investigate the influence of sun activity represented by sunspot numbers on one of the climate indicators, specifically rivers, represented by river flow fluctuation for Daugava, Holston, Nolichucky and French Broad rivers. The Multifractal Detrended Cross-Correlation Analysis (MF-DXA) shows that there exist some crossovers in the cross-correlation fluctuation function versus time scale of the river flow and sunspot series. One of these crossovers corresponds to the well-known cycle of solar activity demonstrating a universal property of the mentioned rivers. The scaling exponent given by DCCA for original series at intermediate time scale, ( 12 – 24 ) ≤ s ≤ 130 months , is λ = 1.17 ± 0.04 which is almost similar for all underlying rivers at 1 σ confidence interval showing the second universal behavior of river runoffs. To remove the sinusoidal trends embedded in data sets, we apply the Singular Value Decomposition (SVD) method. Our results show that there exists a long-range cross-correlation between the sunspot numbers and the underlying streamflow records. The magnitude of the scaling exponent and the corresponding cross-correlation exponent are λ ∈ ( 0.76 , 0.85 ) and γ × ∈ ( 0.30 , 0.48 ) , respectively. Different values for scaling and cross-correlation exponents may be related to local and external factors such as topography, drainage network morphology, human activity and so on. Multifractal cross-correlation analysis demonstrates that all underlying fluctuations have almost weak multifractal nature which is also a universal property for data series. In addition the empirical relation between scaling exponent derived by DCCA and Detrended Fluctuation Analysis (DFA), λ ≈ ( h sun + h river ) / 2 is confirmed.
TL;DR: This paper showed empirically that LDND is the better measure in the situation where the languages compared have not already been shown, by other, more traditional methods of comparative linguistics, to be related If automated language classification is to be used as a tool independent of traditional methods then the further modification is necessary
Abstract: In Ref [13] , Petroni and Serva discuss the use of Levenshtein distances (LD) between words referring to the same concepts as a tool for establishing overall distances among languages which can then subsequently be used to derive phylogenies The authors modify the raw LD by dividing the LD by the length of the longer of the two words compared, to produce what could be called LDN (normalized LD) Other scholars [7] , [8] have used a further modification, where they divide the LDN by the average LDN among words not referring to the same concept This produces what could be called LDND The authors of Ref [13] question whether LDND is a more adequate measure of distance than LDN Here we show empirically that LDND is the better measure in the situation where the languages compared have not already been shown, by other, more traditional methods of comparative linguistics, to be related If automated language classification is to be used as a tool independent of traditional methods then the further modification is necessary
TL;DR: In this paper, the universal first-passage properties of a simple correlated discrete-time sequence up to n steps were discussed, where the position at step i of a random walker hopping on a continuous line by drawing independently, at each time step, a random jump length from an arbitrary symmetric and continuous distribution.
Abstract: In these lecture notes I will discuss the universal first-passage properties of a simple correlated discrete-time sequence { x 0 = 0 , x 1 , x 2 , … , x n } up to n steps where x i represents the position at step i of a random walker hopping on a continuous line by drawing independently, at each time step, a random jump length from an arbitrary symmetric and continuous distribution (it includes, e.g., the Levy flights). I will focus on the statistics of two extreme observables associated with the sequence: (i) its global maximum and the time step at which the maximum occurs and (ii) the number of records in the sequence and their ages. I will demonstrate how the universal statistics of these observables emerge as a consequence of Pollaczek–Spitzer formula and the associated Sparre Andersen theorem.
TL;DR: In this article, the authors investigated the effect of individual heterogeneity by setting the cooperator x an investment value correlated to its degree, where x = N ⋅ k x β / ∑ j k j β, where k x is the degree of x, j runs over all players and β is a tunable parameter.
Abstract: The public goods game (PGG) is generally considered as a suitable paradigm to explain ubiquitous cooperative behavior. In this study, we investigated the evolutionary PGG on scale-free networks and studied the effect of individual heterogeneity by setting the cooperator x an investment value correlated to its degree as I x = N ⋅ k x β / ∑ j k j β , where k x is the degree of x , j runs over all players and β is a tunable parameter. It is shown that the cooperation level is remarkably promoted by negative values of β whereas it is highly depressed by positive values of β . Moreover, the effect of environmental noise has also been investigated. Our result may sharpen the understanding of cooperation induced by the individual diversity.
TL;DR: Two new discrete proliferation models in the context of an exclusion process with an undirected motility mechanism are proposed and analyzed, related to a family of reaction- diffusion equations and can be used to make predictions over a range of scales appropriate for interpreting experimental data.
Abstract: Cell invasion involves a population of cells which are motile and proliferative. Traditional discrete models of proliferation involve agents depositing daughter agents on nearest-neighbor lattice sites. Motivated by time-lapse images of cell invasion, we propose and analyze two new discrete proliferation models in the context of an exclusion process with an undirected motility mechanism. These discrete models are related to a family of reaction–diffusion equations and can be used to make predictions over a range of scales appropriate for interpreting experimental data. The new proliferation mechanisms are biologically relevant and mathematically convenient as the continuum–discrete relationship is more robust for the new proliferation mechanisms relative to traditional approaches.
TL;DR: An equivalence of the objective functions of the symmetric nonnegative matrix factorization (SNMF) and the maximum optimization of modularity density is discussed and a new algorithm, named the so-called SNMF-SS, is developed by combining SNMF and a semi-supervised clustering approach.
Abstract: Discovering a community structure is fundamental for uncovering the links between structure and function in complex networks. In this paper, we discuss an equivalence of the objective functions of the symmetric nonnegative matrix factorization (SNMF) and the maximum optimization of modularity density. Based on this equivalence, we develop a new algorithm, named the so-called SNMF-SS, by combining SNMF and a semi-supervised clustering approach. Previous NMF-based algorithms often suffer from the restriction of measuring network topology from only one perspective, but our algorithm uses a semi-supervised mechanism to get rid of the restriction. The algorithm is illustrated and compared with spectral clustering and NMF by using artificial examples and other classic real world networks. Experimental results show the significance of the proposed approach, particularly, in the cases when community structure is obscure.
TL;DR: An improved cellular automaton model for pedestrian dynamics was established, where both static floor field and collision effect derived from game theory were considered, and proved that outflow rate from an evacuation exit can be improved by placing an appropriate obstacle in front of the exit.
Abstract: An improved cellular automaton model for pedestrian dynamics was established, where both static floor field and collision effect derived from game theory were considered Several model parameters were carefully determined by previous studies Results obtained through model-based simulation and analytical approach (derived from mean field approximation) proved that outflow rate from an evacuation exit, which is usually estimated using outflow coefficient in building codes in Japan, can be improved by placing an appropriate obstacle in front of the exit This can reduce collision probability at the exit by increasing collisions around the obstacles ahead of the exit
TL;DR: In this paper, the detection of long range dependence (LRD) is an important task in time series analysis, which is summarized by the well-known Hurst parameter (or exponent) H ∈ [ 0, 1 ], which can be estimated by a number of methods.
Abstract: The detection of long range dependence (LRD) is an important task in time series analysis. LRD is often summarized by the well-known Hurst parameter (or exponent) H ∈ [ 0 , 1 ] , which can be estimated by a number of methods. Some of these techniques are designed to be applied to signals behaving as a stationary fractional Gaussian noise (fGn), whereas others imply that the analyzed time series behave as a non-stationary fractional Brownian motion (fBm). Moreover, some estimators do not yield the Hurst parameter but indexes related to H and ranging outside the unit interval. Therefore, the fGn or fBm nature of the studied time series has to be preliminarily analyzed before applying any estimation method, and the relationships between H and the indexes resulting from the analyses have to be taken into account to obtain coherent results. Since fGn-like series represent the increments of fBm-like processes and both the signals are characterized by the same H value by definition, estimators designed for fGn-like series can be applied to fBm-like sequences after preventive differentiation, and conversely estimators designed for fBm-like processes can be applied to fGn-like series after preventive integration. The signal characterization is particularly important when H is estimated on financial time series because the returns represent the first difference of price time series, which are often assumed to behave like self-affine sequences. The analysis of simulated fGn and fBm time series shows that all the considered methods yield comparable H values when properly applied. The reanalysis of several market price time series already studied in the literature points out that a correct application of the estimators (supported by a preventive signal classification) yields homogeneous H values allowing for a useful cross-validation of results reported in different works. Moreover, some conclusions reported in the literature about the anti-persistence of some financial series are shown to be incorrect because of the inappropriate application of the estimation methods.
TL;DR: In this article, the authors investigate three kinds of fractional differential models (distributed-order model, variable-order and randomorder model) for characterization of anomalous diffusion and highlight the characteristics, physical advantages and potential applications of each model.
Abstract: In this study, we investigate three kinds of fractional differential models (distributed-order model, variable-order model and random-order model) for characterization of anomalous diffusion. The characteristics, physical advantages and potential applications of each model are highlighted. The numerical simulations also validate our analytical and comparison results. Furthermore, a generalized distributed–variable-order model and a more generalized distributed–variable–random-order model are proposed to combine the advantages of distributed-order model, variable-order model and random-order model.