Puente-Ajovin, MiguelRamos, ArturoSanz-Gracia, FernandoPena, Guillermo...
15页查看更多>>摘要：We study the parametric distribution of log-growth rates of CO2 and CO2 per capita emissions for 207 countries and territories taking data from 1994 to 2010. We define the log-growth rates for different duration periods, from one year apart to fifteen years apart. The considered probability distributions have been the following: the normal (N), the asymmetric double Laplace normal (adLN), the exponential tails normal (ETN) and a mixture of two normal (2N) or three normal (3N) distributions. The main result is that the best one is different depending on the period considered, in such a way that there is not a systematically dominant distribution. Thus, the behavior may change from one year to the next one, and possibly this is influenced by policy measures such as the Kyoto protocol or the Clean Development Mechanism. Moreover, a policy measure that can be derived from this paper is that some countries can still reduce their emissions of CO2 compared with others, as seen by the non-uniformity of the preferred probability distribution for each period. We also model a stochastic differential equation whose associated Fokker-Planck equation has as a solution the observed time-dependent probability density function. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:
NSTL
Elsevier
Reyes, LeonardoLaroze, David
8页查看更多>>摘要：Complex adaptive systems can be modelled with Networks and Cellular Automata (CA). In the present work, we study the Greenberg-Hastings (GH) cellular automata running in the Watts-Strogatz (WS) network model. We are interested in finding the conditions under which the system operates near a critical point. We introduce the notion of leverage point in such a simple kind of model: a point in parameter space, at criticality, such that we can change the collective state of the system with a minimal effort. Within our proposed framework, the system's response to changes in disorder is maximal at the leverage point. The GH CA includes a transmission coefficient r that sets the threshold size in the dynamics. We evaluate numerically the critical transmission coefficient rc as a function of the average coordination number of the network K and of the rewiring probability p, where p controls the fluctuations in the coordination number. There is an interval of values in the transmission coefficient r for which the collective state of the system depends on network disorder. This interval narrows as the average coordination number increases and only within it we can tune for criticality by changing disorder alone. Our results are relevant for systems that operate at criticality in order to increase their dynamic range or to operate under optimal information-processing conditions. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Wang, YingZheng, YunanShi, XueleiLiu, Yiguang...
15页查看更多>>摘要：Influence maximization is of great significance in complex networks, and many methods have been proposed to solve it. However, they are usually time-consuming or cannot deal with the overlap of spreading. To get over the flaws, an effective heuristic clustering algorithm is proposed in this paper: (1) nodes that have been assigned to clusters are excluded from the network structure to guarantee they do not participate in subsequent clustering. (2) the K-shell (k(s)) and Neighborhood Coreness (NC) value of nodes in the remaining network are recalculated, which ensures the node influence can be adjusted during the clustering process. (3) a hub node and a routing node are selected for each cluster to jointly determine the initial spreader, which balances the local and global influence. Due to the above contributions, the proposed method preferably guarantees the influence of initial spreaders and the dispersity between them. A series of experiments based on Susceptible-Infected-Recovered (SIR) stochastic model confirm that the proposed method has favorable performance under different initial constraints against known methods, including VoteRank, HC, GCC, HGD, and DLS-AHC. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Wu, LianrenQi, JiayinShi, NanLi, Jinjie...
10页查看更多>>摘要：In social networks, how human activity patterns affect the popularity of topics has always been the focus of research. In this paper, a quantitative temporal analysis of the dynamics of topics popularity in Sina Weibo system was provided. Firstly, the popularity time series of 1167 topics were clustered into four clusters by K-Spectral Centroid (KSC) clustering algorithm. Secondly, for each cluster, we calculated the exponents of topic popularity decay distribution alpha and the exponents of inter-activity time distribution beta, respectively. Two interesting results were found: one is that the peak fraction F of topics popularity positively correlated with the topics popularity decay exponent alpha; the other is that bursty activity patterns in social network significantly affect topics popularity dynamics: there is a positive correlation between exponent alpha and exponent beta. Finally, we proposed an extended SI (susceptible-infected) epidemic model with incorporate bursty human activity and verified the results by simulation. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Xu, Xin-JianChen, ChengMendes, J. F. F.
7页查看更多>>摘要：Quantifying dissimilarities between networks is a fundamental and challenging problem in network science. Current metrics for network comparison either assume the homogeneous distribution of nodal degrees or ignore the community structure of the network. Here we propose an efficient measure for comparing heterogeneous networks with communities from the perspective of probability distribution functions, which incorporates the nodal distance distribution, the clustering coefficient distribution and the alpha centrality distribution. Comparison between community benchmarks shows that the proposed measure returns non-zero values only when the networks are non-isomorphic. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Li, Jiang-ChengTao, ChenLi, Hai-Feng
16页查看更多>>摘要：In a complex financial system, what is the forecasting performance of macro and micro evolution models of Econophysics on asset prices? For this problem, from the perspective of machine learning, we study the dynamic forecasting and liquidity assessment of financial markets, based on econophysics and Bayesian methods. We establish eight dynamic prediction methods, based on our proposed likelihood estimation and Bayesian estimation methods of macro and micro evolution models of econophysics. Combined machine learning thinking and real data, we empirically study and simulate the out-of-sample dynamic forecasting analysis of eight proposed methods and compare with the benchmark GARCH model. A variety of loss functions, superior predictive ability test (SPA), Akaike and Bayesian information criterion (AIC and BIC) methods are introduced to further evaluate the forecasting performance of our proposed methods. The research of out of sample prediction shows that (1) the method of the simplified stochastic model with Bayesian method for only sample return has the best forecasting performance; (2) the method of the stochastic model with Bayesian method for only return samples has the worst forecasting performance. For the liquidity assessment problem, there is a strong correlation between the trading probability evaluated by the proposed eight methods and the real turnover rate, and an increase of liquidity is correspond to the increase of asset risk. In other words, it suggests that all proposed methods can well evaluate market liquidity. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
