Showing papers in "Physica A-statistical Mechanics and Its Applications in 2013"
TL;DR: A survey of the most relevant scientific studies investigating the properties of different Power Grids infrastructures using Complex Network Analysis techniques and methodologies and traces the evolution in such field of the approach of study during the years to see the improvement achieved in the analysis.
Abstract: The statistical tools of Complex Network Analysis are of useful to understand salient properties of complex systems, may these be natural or pertaining human engineered infrastructures. One of these that is receiving growing attention for its societal relevance is that of electricity distribution. In this paper, we present a survey of the most relevant scientific studies investigating the properties of different Power Grids infrastructures using Complex Network Analysis techniques and methodologies. We categorize and explore the most relevant literature works considering general topological properties, physical properties, and differences between the various graph-related indicators and reliability aspects. We also trace the evolution in such field of the approach of study during the years to see the improvement achieved in the analysis.
586 citations
TL;DR: The connections between various features of human behavior extracted from a large mobile phone dataset are explored and it is shown that clustering and principal component analysis allow for a significant dimension reduction with limited loss of information.
Abstract: Mobile phone datasets allow for the analysis of human behavior on an unprecedented scale. The social network, temporal dynamics and mobile behavior of mobile phone users have often been analyzed independently from each other using mobile phone datasets. In this article, we explore the connections between various features of human behavior extracted from a large mobile phone dataset. Our observations are based on the analysis of communication data of 100000 anonymized and randomly chosen individuals in a dataset of communications in Portugal. We show that clustering and principal component analysis allow for a significant dimension reduction with limited loss of information. The most important features are related to geographical location. In particular, we observe that most people spend most of their time at only a few locations. With the help of clustering methods, we then robustly identify home and oce locations and compare the results with ocial census data. Finally, we analyze the geographic spread of users’ frequent locations and show that commuting distances can be reasonably well explained by a gravity model.
245 citations
TL;DR: This paper modifies a flow chart of the rumor spreading process with the SIR (Susceptible, Infected, and Recovered) model, and thus makes the belief that ignorants will inevitably change their status once they are made aware of a rumor by spreaders.
Abstract: With the prevalence of new media, e.g., microblogging, rumors spread faster and wider than ever before. On the basis of prior studies, this paper modifies a flow chart of the rumor spreading process with the SIR (Susceptible, Infected, and Recovered) model, and thus makes the rumor spreading process more realistic and apparent. The authors believe that ignorants will inevitably change their status once they are made aware of a rumor by spreaders; the probabilities that a spreader becomes a stifler are differentiated in accordance with reality. In the numerical simulation part, the impact that variations of different parameters have on the rumor spreading process will be analyzed.
232 citations
TL;DR: In this article, a new lattice hydrodynamic traffic flow model is proposed by considering the driver's anticipation effect in sensing relative flux (DAESRF) for two-lane system.
Abstract: In this paper, a new lattice hydrodynamic traffic flow model is proposed by considering the driver’s anticipation effect in sensing relative flux (DAESRF) for two-lane system. The effect of anticipation parameter on the stability of traffic flow is examined through linear stability analysis and shown that the anticipation term can significantly enlarge the stability region on the phase diagram. To describe the phase transition of traffic flow, mKdV equation near the critical point is derived through nonlinear analysis. The theoretical findings have been verified using numerical simulation which confirms that traffic jam can be suppressed efficiently by considering the anticipation effect in the new lattice model for two-lane traffic.
213 citations
TL;DR: Comparing with the epidemic process results for four real networks and the Barabasi–Albert network, the parameterless method could identify the node spreading influence more accurately than the ones generated by the degree k, closeness centrality, k-shell and mixed degree decomposition methods.
Abstract: Identifying the node spreading influence in networks is an important task to optimally use the network structure and ensure the more efficient spreading in information. In this paper, by taking into account the shortest distance between a target node and the node set with the highest k -core value, we present an improved method to generate the ranking list to evaluate the node spreading influence. Comparing with the epidemic process results for four real networks and the Barabasi–Albert network, the parameterless method could identify the node spreading influence more accurately than the ones generated by the degree k , closeness centrality, k -shell and mixed degree decomposition methods. This work would be helpful for deeply understanding the node importance of a network.
212 citations
TL;DR: A new centrality measure is proposed based on the Dempster–Shafer evidence theory, which trades off between the degree and strength of every node in a weighted network.
Abstract: The design of an effective ranking method to identify influential nodes is an important problem in the study of complex networks. In this paper, a new centrality measure is proposed based on the Dempster–Shafer evidence theory. The proposed measure trades off between the degree and strength of every node in a weighted network. The influences of both the degree and the strength of each node are represented by basic probability assignment (BPA). The proposed centrality measure is determined by the combination of these BPAs. Numerical examples are used to illustrate the effectiveness of the proposed method.
197 citations
TL;DR: A community detection method based on modularity and an improved genetic algorithm (MIGA) is put forward, which takes the modularity Q as the objective function, and uses prior information, which makes the algorithm more targeted and improves the stability and accuracy of community detection.
Abstract: Complex networks are widely applied in every aspect of human society, and community detection is a research hotspot in complex networks. Many algorithms use modularity as the objective function, which can simplify the algorithm. In this paper, a community detection method based on modularity and an improved genetic algorithm (MIGA) is put forward. MIGA takes the modularity Q as the objective function, which can simplify the algorithm, and uses prior information (the number of community structures), which makes the algorithm more targeted and improves the stability and accuracy of community detection. Meanwhile, MIGA takes the simulated annealing method as the local search method, which can improve the ability of local search by adjusting the parameters. Compared with the state-of-art algorithms, simulation results on computer-generated and four real-world networks reflect the effectiveness of MIGA.
180 citations
TL;DR: A new anticipation optimal velocity model (AOVM) is proposed by considering anticipation effect on the basis of the full velocity difference model (FVDM) for car-following theory on single lane and numerical simulation shows that AOVM might avoid the disadvantage of negative velocity and headway that occur at small sensitivity coefficients in the FVDM.
Abstract: In this paper, a new anticipation optimal velocity model (AOVM) is proposed by considering anticipation effect on the basis of the full velocity difference model (FVDM) for car-following theory on single lane. The linear stability condition is derived from linear stability analysis. Starting and braking process is investigated for the car motion under a traffic signal, which shows that the results accord with empirical traffic values. Especially AOVM can avoid the disadvantage of the unrealistically high deceleration appearing in FVDM. Furthermore, numerical simulation shows that AOVM might avoid the disadvantage of negative velocity and headway that occur at small sensitivity coefficients in the FVDM since the anticipation effect is taken into account in AOVM, which means that collision disappears with the consideration of an appropriate anticipation parameter.
169 citations
TL;DR: For conducting short-term time series analysis, this study shows that the modified multiscale entropy algorithm is more reliable than the conventional MSE.
Abstract: Multiscale entropy (MSE) is a prevalent algorithm used to measure the complexity of a time series. Because the coarse-graining procedure reduces the length of a time series, the conventional MSE algorithm applied to a short-term time series may yield an imprecise estimation of entropy or induce undefined entropy. To overcome this obstacle, the modified multiscale entropy (MMSE) was developed. The coarse-graining procedure was replaced with a moving-average procedure and a time delay was incorporated for constructing template vectors in calculating sample entropy. For conducting short-term time series analysis, this study shows that the MMSE algorithm is more reliable than the conventional MSE.
165 citations
TL;DR: In this paper, the authors introduced a new measure for capital market efficiency, which takes into consideration the correlation structure of the returns (long-term and short-term memory) and local herding behavior (fractal dimension).
Abstract: We introduce a new measure for capital market efficiency. The measure takes into consideration the correlation structure of the returns (long-term and short-term memory) and local herding behavior (fractal dimension). The efficiency measure is taken as a distance from an ideal efficient market situation. The proposed methodology is applied to a portfolio of 41 stock indices. We find that the Japanese NIKKEI is the most efficient market. From a geographical point of view, the more efficient markets are dominated by the European stock indices and the less efficient markets cover mainly Latin America, Asia and Oceania. The inefficiency is mainly driven by a local herding, i.e. a low fractal dimension.
149 citations
TL;DR: The SIHR rumor spreading model with consideration of the forgetting and remembering mechanisms was studied in homogeneous networks is refined and mean-field equations are derived to describe the dynamics of the rumors spreading model in inhomogeneous networks.
Abstract: The SIHR rumor spreading model with consideration of the forgetting and remembering mechanisms was studied in homogeneous networks. We further investigate the properties of the SIHR model in inhomogeneous networks. The SIHR model is refined and mean-field equations are derived to describe the dynamics of the rumor spreading model in inhomogeneous networks. Steady-state analysis is carried out, which shows no spreading threshold existing. Numerical simulations are conducted in a BA scale-free network. The simulation results show that the network topology exerts significant influences on the rumor spreading: In comparison with the ER network, the rumor spreads faster and the final size of the rumor is smaller in BA scale-free network; the forgetting and remembering mechanisms greatly impact the final size of the rumor. Finally, through the numerical simulation, we examine the effects that the spreading rate and the stifling rate have on the the influence of the rumor. In addition, the no threshold result is verified.
TL;DR: The three-phase traffic theory is discussed as the basis for traffic flow modeling as well as the network breakdown minimization (BM) principle for the optimization of traffic and transportation networks with road bottlenecks.
Abstract: It is explained why the set of the fundamental empirical features of traffic breakdown (a transition from free flow to congested traffic) should be the empirical basis for any traffic and transportation theory that can be reliably used for control and optimization in traffic networks. It is shown that the generally accepted fundamentals and methodologies of the traffic and transportation theory are not consistent with the set of the fundamental empirical features of traffic breakdown at a highway bottleneck. To these fundamentals and methodologies of the traffic and transportation theory belong (i) Lighthill–Whitham–Richards (LWR) theory, (ii) the General Motors (GM) model class (for example, Herman, Gazis et al. GM model, Gipps’s model, Payne’s model, Newell’s optimal velocity (OV) model, Wiedemann’s model, Bando et al. OV model, Treiber’s IDM, Kraus’s model), (iii) the understanding of highway capacity as a particular (fixed or stochastic) value, and (iv) principles for traffic and transportation network optimization and control (for example, Wardrop’s user equilibrium (UE) and system optimum (SO) principles). Alternatively to these generally accepted fundamentals and methodologies of the traffic and transportation theory, we discuss the three-phase traffic theory as the basis for traffic flow modeling as well as briefly consider the network breakdown minimization (BM) principle for the optimization of traffic and transportation networks with road bottlenecks.
TL;DR: A new Evidential Semi-local Centrality (ESC) is proposed by modifying EVC in two aspects, and the Basic Probability Assignment (BPA) of degree generated by EVC is modified according to the actual degree distribution, rather than just following uniform distribution.
Abstract: How to identify influential nodes in complex networks is still an open hot issue. In the existing evidential centrality (EVC), node degree distribution in complex networks is not taken into consideration. In addition, the global structure information has also been neglected. In this paper, a new Evidential Semi-local Centrality (ESC) is proposed by modifying EVC in two aspects. Firstly, the Basic Probability Assignment (BPA) of degree generated by EVC is modified according to the actual degree distribution, rather than just following uniform distribution. BPA is the generation of probability in order to model uncertainty. Secondly, semi-local centrality combined with modified EVC is extended to be applied in weighted networks. Numerical examples are used to illustrate the efficiency of the proposed method.
TL;DR: In this paper, the authors discuss the dynamics of a stochastic SIS epidemic model with vaccination and show that when the noise is large, the infective decays exponentially to zero regardless of the magnitude of R 0.
Abstract: In this paper, we discuss the dynamics of a stochastic SIS epidemic model with vaccination. When the noise is large, the infective decays exponentially to zero regardless of the magnitude of R 0 . When the noise is small, sufficient conditions for extinction exponentially and persistence in the mean are established. The results are illustrated by computer simulations.
TL;DR: Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus, however, given power to a random selection of nodes could not improve consensus properties.
Abstract: In this paper we investigate the effects of social power on the evolution of opinions in model networks as well as in a number of real social networks. A continuous opinion formation model is considered and the analysis is performed through numerical simulation. Social power is given to a proportion of agents selected either randomly or based on their degrees. As artificial network structures, we consider scale-free networks constructed through preferential attachment and Watts–Strogatz networks. Numerical simulations show that scale-free networks with degree-based social power on the hub nodes have an optimal case where the largest number of the nodes reaches a consensus. However, given power to a random selection of nodes could not improve consensus properties. Introducing social power in Watts–Strogatz networks could not significantly change the consensus profile.
TL;DR: In this paper, a dynamical mean field theory is used to predict the end-monomer mean square displacement of single-stranded DNA and finally estimate two important parameters, the persistence length l p and the length per base l d, and finally reach optimum values by fitting the theoretical data to the experimental data of Shusterman et al.
Abstract: A dynamical mean field theory is used to predict the end-monomer mean square displacement of single-stranded DNA and finally estimate two important parameters—the persistence length l p and the length per base l d . Both parameters are set free, and finally reach optimum values by fitting the theoretical data to the experimental data of Shusterman et al. [R. Shusterman, S. Alon, T. Gavrinyov, O. Krichevsky, Monomer dynamics in double- and single-stranded DNA polymers, Phys. Rev. Lett. 92 (2004) 048303]. Three optimization methods, global optimization, individual optimization and selected optimization are performed with the Monte Carlo method. All the optimization methods can faithfully reproduce the experimental data. In selected optimization for 2400 and 6700 bases ssDNA, l p = 2.223 nm and l d = 0.676 nm are obtained. The theoretical results show a larger persistence length for ssDNA than ordinary synthetic polymers, and the obtained length per base is larger than the reported value obtained from single molecule force measurements. The l p and l d obtained from mean field theory complement the current data previously measured for different salt concentrations in solution.
TL;DR: This work proposes an extension of Watts’s classic threshold model to temporal networks by assuming that an agent is influenced by contacts which lie a certain time into the past, I.e., the individuals are affected by contacts within a time window.
Abstract: Threshold models try to explain the consequences of social influence like the spread of fads and opinions. Along with models of epidemics, they constitute a major theoretical framework of social sp ...
TL;DR: The empirical cross-correlation matrices constructed by the DCCA coefficient show several interesting properties at different time scales in the US stock market, which are useful to the risk management and optimal portfolio selection, especially to the diversity of the asset portfolio.
Abstract: In this study, we first build two empirical cross-correlation matrices in the US stock market by two different methods, namely the Pearson’s correlation coefficient and the detrended cross-correlation coefficient (DCCA coefficient). Then, combining the two matrices with the method of random matrix theory (RMT), we mainly investigate the statistical properties of cross-correlations in the US stock market. We choose the daily closing prices of 462 constituent stocks of S&P 500 index as the research objects and select the sample data from January 3, 2005 to August 31, 2012. In the empirical analysis, we examine the statistical properties of cross-correlation coefficients, the distribution of eigenvalues, the distribution of eigenvector components, and the inverse participation ratio. From the two methods, we find some new results of the cross-correlations in the US stock market in our study, which are different from the conclusions reached by previous studies. The empirical cross-correlation matrices constructed by the DCCA coefficient show several interesting properties at different time scales in the US stock market, which are useful to the risk management and optimal portfolio selection, especially to the diversity of the asset portfolio. It will be an interesting and meaningful work to find the theoretical eigenvalue distribution of a completely random matrix R for the DCCA coefficient because it does not obey the Marcenko–Pastur distribution.
TL;DR: In this article, the authors examined the asymmetric multifractal scaling behavior of Chinese stock markets with uptrends or downtrends and found that the multifractality degree of Chinese stocks with uptending is stronger than that of stocks with downtending.
Abstract: We utilized asymmetric multifractal detrended fluctuation analysis in this study to examine the asymmetric multifractal scaling behavior of Chinese stock markets with uptrends or downtrends. Results show that the multifractality degree of Chinese stock markets with uptrends is stronger than that of Chinese stock markets with downtrends. Correlation asymmetries are more evident in large fluctuations than in small fluctuations. By discussing the source of asymmetric multifractality, we find that multifractality is related to long-range correlations when the market is going up, whereas it is related to fat-tailed distribution when the market is going down. The main source of asymmetric scaling behavior in the Shanghai stock market are long-range correlations, whereas that in the Shenzhen stock market is fat-tailed distribution. An analysis of the time-varying feature of scaling asymmetries shows that the evolution trends of these scaling asymmetries are similar in the two Chinese stock markets. Major financial and economical events may enhance scaling asymmetries.
TL;DR: It is demonstrated that the average degree of neurons within the hybrid scale-free network significantly influences the optimal amount of noise for the occurrence of stochastic resonance, indicating that there also exists an optimal topology for the amplification of the response to the weak input signal.
Abstract: We study the phenomenon of stochastic resonance in a system of coupled neurons that are globally excited by a weak periodic input signal. We make the realistic assumption that the chemical and electrical synapses interact in the same neuronal network, hence constituting a hybrid network. By considering a hybrid coupling scheme embedded in the scale-free topology, we show that the electrical synapses are more efficient than chemical synapses in promoting the best correlation between the weak input signal and the response of the system. We also demonstrate that the average degree of neurons within the hybrid scale-free network significantly influences the optimal amount of noise for the occurrence of stochastic resonance, indicating that there also exists an optimal topology for the amplification of the response to the weak input signal. Lastly, we verify that the presented results are robust to variations of the system size.
TL;DR: The effects of delaying the time to recovery (delayed recovery) and of nonuniform transmission on the propagation of diseases on structured populations are investigated through a mean-field approximation and large-scale numerical simulations.
Abstract: We investigate the effects of delaying the time to recovery (delayed recovery) and of nonuniform transmission on the propagation of diseases on structured populations. Through a mean-field approximation and large-scale numerical simulations, we find that postponing the transition from the infectious to the recovered states can largely reduce the epidemic threshold, therefore promoting the outbreak of epidemics. On the other hand, if we consider nonuniform transmission among individuals, the epidemic threshold increases, thus inhibiting the spreading process. When both mechanisms are at work, the latter might prevail, hence resulting in an increase of the epidemic threshold with respect to the standard case, in which both ingredients are absent. Our findings are of interest for a better understanding of how diseases propagate on structured populations and to a further design of efficient immunization strategies.
TL;DR: Wang et al. as discussed by the authors investigated the cross-correlations between the stock market in China and markets in Japan, South Korea and Hong Kong, and found that the crosscorrelations display the characteristic of multifractality in the short term.
Abstract: In this paper, we investigate the cross-correlations between the stock market in China and markets in Japan, South Korea and Hong Kong. We use not only the qualitative analysis of the cross-correlation test, but also the quantitative analysis of the MF-X-DFA. Our findings confirm the existence of cross-correlations between the stock market in China and markets in Japan, South Korea and Hong Kong, which have strongly multifractal features. We find that the cross-correlations display the characteristic of multifractality in the short term. Moreover, the cross-correlations of small fluctuations are persistent and those of large fluctuations are anti-persistent in the short term, while the cross-correlations of all kinds of fluctuations are persistent in the long term. Furthermore, based on the multifractal spectrum, we also find that the multifractality of cross-correlation between stock markets in China and Japan are stronger than those between China and South Korea, as well as between China and Hong Kong.
TL;DR: In this paper, two apparently opposed principles for their rate of entropy production have been proposed: the minimum entropy production rate and the maximum entropy production, useful in the analysis of dissipation and irreversibility of different processes in physics, chemistry, biology and engineering.
Abstract: Open systems are very important in science and engineering for their applications and the analysis of the real word. At their steady state, two apparently opposed principles for their rate of entropy production have been proposed: the minimum entropy production rate and the maximum entropy production, useful in the analysis of dissipation and irreversibility of different processes in physics, chemistry, biology and engineering. Both principles involve an extremum of the rate of the entropy production at the steady state under non-equilibrium conditions. On the other hand, in engineering thermodynamics, dissipation and irreversibility are analyzed using the entropy generation, for which there exist two principle of extrema too, the minimum and the maximum principle. Finally, oppositions to the extrema principle have been developed too. In this paper, all these extrema principles will be analyzed in order to point out the relations among them and a synthesis useful in engineering applications, in physical and chemical process analysis and in biology and biotechnology will be proposed.
TL;DR: In this article, it was shown that the maximum entropy principle (MEP) gives the same closure of the system as that obtained in the phenomenological ET theory with 14 fields discussed in Chap. 5.
Abstract: In this chapter, we prove, in the case of polyatomic rarefied gases, that the maximum entropy principle (MEP) gives the same closure of the system as that obtained in the phenomenological ET theory with 14 fields discussed in Chap. 5 The main idea is to consider a generalized distribution function depending not only on the velocity but also on an extra variable that connects with the internal degrees of freedom of a constituent molecule. On the basis of MEP, we again obtain the same binary hierarchy introduced in the previous chapter: the one is the usual momentum-type, F-series, and the other is the energy-type, G-series. The extra variable plays a role in the G-series. Thus we prove the perfect agreement between the ET theory and the molecular ET theory at least within 14-field theories. The agreement for any number of moments will be proved in Chap. 10
TL;DR: This paper introduces a shortest-remaining-path-first queuing strategy into a network traffic model on Barabasi–Albert scale-free networks under efficient routing protocol, where one packet’s delivery priority is related to its current distance to the destination.
Abstract: With rapid economic and social development, the problem of traffic congestion is getting more and more serious. Accordingly, network traffic models have attracted extensive attention. In this paper, we introduce a shortest-remaining-path-first queuing strategy into a network traffic model on Barabasi–Albert scale-free networks under efficient routing protocol, where one packet’s delivery priority is related to its current distance to the destination. Compared with the traditional first-in-first-out queuing strategy, although the network capacity has no evident changes, some other indexes reflecting transportation efficiency are significantly improved in the congestion state. Extensive simulation results and discussions are carried out to explain the phenomena. Our work may be helpful for the designing of optimal networked-traffic systems.
TL;DR: With this proposed methodology, metaheuristic searches are not longer necessary and one can resort solely to rigorous controlled local search algorithms, leading to a dramatic increase in efficiency.
Abstract: We present a simple transformation of the formulation of the log-periodic power law formula of the Johansen–Ledoit–Sornette (JLS) model of financial bubbles that reduces it to a function of only three nonlinear parameters. The transformation significantly decreases the complexity of the fitting procedure and improves its stability tremendously because the modified cost function is now characterized by good smooth properties with in general a single minimum in the case where the model is appropriate to the empirical data. We complement the approach with an additional subordination procedure that slaves two of the nonlinear parameters to the most crucial nonlinear parameter, the critical time t c , defined in the JLS model as the end of the bubble and the most probable time for a crash to occur. This further decreases the complexity of the search and provides an intuitive representation of the results of the calibration. With our proposed methodology, metaheuristic searches are not longer necessary and one can resort solely to rigorous controlled local search algorithms, leading to a dramatic increase in efficiency. Empirical tests on the Shanghai Composite index (SSE) from January 2007 to March 2008 illustrate our findings.
TL;DR: This work studies the structure of inter- industry relationships using networks of money flows between industries in 45 national economies to find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure.
Abstract: We study the structure of inter-industry relationships using networks of money flows between industries in 45 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, service industries, and extraction industries.
TL;DR: A well-defined relationship between αDFA and λDCCA, respectively described by the DFA and DCCA methods, is established theoretically and for some specific times series, this theory is applied in order to establish its validity.
Abstract: We propose in this paper to establish a well-defined relationship between α D F A (the long range auto-correlation exponent) and λ D C C A (the long range cross-correlation exponent), respectively described by the DFA and DCCA methods. This relationship will be accomplished theoretically by differentiating the DCCA cross-correlation coefficient, ρ D C C A ( n ) . Also, for some specific times series, we apply this theory in order to establish its validity.
TL;DR: In this article, the authors show that only a combination of plots can give some degree of confidence about the real presence of Paretianity in the data, and they propose some additional tools that can be used to refine the analysis.
Abstract: Pareto distributions, and power laws in general, have demonstrated to be very useful models to describe very different phenomena, from physics to finance. In recent years, the econophysical literature has proposed a large amount of papers and models justifying the presence of power laws in economic data. Most of the times, this Paretianity is inferred from the observation of some plots, such as the Zipf plot and the mean excess plot. If the Zipf plot looks almost linear, then everything is ok and the parameters of the Pareto distribution are estimated. Often with OLS. Unfortunately, as we show in this paper, these heuristic graphical tools are not reliable. To be more exact, we show that only a combination of plots can give some degree of confidence about the real presence of Paretianity in the data. We start by reviewing some of the most important plots, discussing their points of strength and weakness, and then we propose some additional tools that can be used to refine the analysis.
TL;DR: A social network based human dynamics model is proposed, and it is pointed out that inducing drive and spontaneous drive lead to the behavior of posting microblogs.
Abstract: The influence of microblog on information transmission is becoming more and more obvious. By characterizing the behavior of following and being followed as out-degree and in-degree respectively, a microblog social network was built in this paper. It was found to have short diameter of connected graph, short average path length and high average clustering coefficient. The distributions of out-degree, in-degree and total number of microblogs posted present power-law characters. The exponent of total number distribution of microblogs is negatively correlated with the degree of each user. With the increase of degree, the exponent decreases much slower. Based on empirical analysis, we proposed a social network based human dynamics model in this paper, and pointed out that inducing drive and spontaneous drive lead to the behavior of posting microblogs. The simulation results of our model match well with practical situation.