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
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
Zhao, XueyiNing, DiDeng, Lebin
15页查看更多>>摘要：In the past two decades, large interconnected systems have been modeled as single-layer networks. In fact, various networks may interact and influence with each other to form the hypernetworks. As one of the most important problems in network science, topology identification of complex network has been widely studied in single-layer networks. On the contrary, topology identification of hypernetwork has received little attention. For a hypernetwork, different layers may have different delay, and noise is usually unavoidable. Therefore, the delayed hypernetwork with stochastic perturbation is put forward in this manuscript. By using synchronization-based identification method, the schemes to identify the unknown topology and system parameters are proposed. It is found that the unknown topology and system parameters of the drive network can be correctly identified, regardless of whether the topology of the response network is the same as that of the drive network. Numerical examples are illustrated to verify the effectiveness of the proposed algorithm. In addition, it is found that the identification time increases with the varying interval of coupling strengths, and the interval of the time delays and stochastic perturbations have opposite effects on the identification time. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Ramadoss, JanarthananKengne, JacquesTelem, Adelaide Nicole KengnouRajagopal, Karthikeyan...
20页查看更多>>摘要：Y We investigate the impact of a broken symmetry on the dynamics of the well-known Shinriki oscillator. The broken symmetry is caused by the memristive diodes bridge with an asymmetric pinched hysteresis loop current-voltage characteristic designed by selecting two pairs of semiconductor diodes with different electrical properties. We examine how the broken symmetry affects the topology of attractors, the nature of fixed points, the bifurcation structures, the number and types of coexisting solutions, and the topology of the basins of attraction as well. These features are highlighted by utilizing plots of Lyapunov exponents, bifurcation diagrams, basins of attraction and phase portraits. As sample results, up to four coexisting asymmetric chaotic and periodic attractors are reported following changes in both initial conditions and parameters. Moreover, some sets of parameters are revealed where the system develops the striking feature of coexisting bubbles of bifurcation. Breadboard experiments are carried out to support the theoretical investigations. Although the breaking of symmetry may be seen as a common practice to discover new nonlinear events, the results obtained in this work may help for a better understanding of the impact of a real memristor on the global behavior of chaotic oscillators including a memristor as nonlinear device. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Liu, WeiChang, ZhenhaiJia, CaiyanZheng, Yimei...
14页查看更多>>摘要：A challenge of community detection in attributed networks is how we can design an effective and efficient clustering method that can not only discover a wide of structure types but also have good community semantic annotations. To this end, by sharing the latent position of nodes, a mathematically principled model (named GNAN) that fuses topological information and node-attribute information is developed. Using the expectation-maximization algorithm, the latent position of each node and the model parameters are learned. The new model detects communities more accurately than can be done with topology information alone. And a case study is provided to show the ability of our model in the semantic interpretability of communities. In detail, firstly, inspired by the idea of NMM (Newman's Mixture Models), a group of parameters that characterize the link behaviors of nodes is introduced into the topological model. In the probabilistic sense, nodes with the same link pattern form a community. Therefore, the combined model can generate not only traditional communities, i.e., groupings of nodes with dense internal connections and sparse external ones, but also a range of other types of structure in networks, such as bipartite structure, core-periphery structure, and their mixture structure, which are collectively referred to as generalized structure. Secondly, based on the homogeneity assumption, another group of parameters describing the distribution of attributes in a community is introduced into the attributed model. Under the control of these parameters, the united model can generate different attributes according to the probability, and automatically discover the critical attributes of the community. Finally, experiments on both synthetic and real-world networks with various network structures show that the new model can detect communities more accurately than the related state-of-the-art models. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Wu, YanqiZhang, JunfengLin, Peng
14页查看更多>>摘要：Faults of components may deteriorate the system performances and stochastic cyber attacks may destroy the state measurements of systems and obstruct control commands. This paper proposes a non-fragile hybrid-triggered control design for networked positive switched systems to deal with the faults and cyber attacks. Bernoulli distribution is employed for describing cyber attacks. Based on the property of cyber attacks, a hybrid-triggered strategy is introduced. The corresponding hybrid-triggered mechanism consists of time- and event-triggered schemes. The event-triggering condition is established in a linear form. By combining a matrix decomposition approach and switched co-positive Lyapunov functions, a non-fragile hybrid-triggered controller is designed for positive switched systems with actuator faults. Then, a stochastic hybrid control strategy is used for the systems subject to stochastic cyber attacks. Under the hybrid-triggered controller, the systems can resist the risk from the attacks and actuator faults. Meanwhile, the risk of communication congestion from the limited bandwidth is reduced by virtue of the hybrid-triggered control strategy. All the presented conditions can be solved via linear programming. Finally, two examples are given to verify the effectiveness of the designed controller. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Jindal, A.Bhatia, N.Kolomeisky, A. B.Gupta, A. K....
16页查看更多>>摘要：Motor proteins or biological molecular motors belong to a class of active enzymatic molecules that are responsible for transport and force generation in living cells. They typically operate in large teams and individual protein molecules interact with each other while moving along linear cytoskeleton filaments. Moreover, during their transportation the motors can reversibly dissociate from their tracks. Motivated by these observations, we propose a one dimensional totally asymmetric simple exclusion model for interacting particles that are allowed to reversibly dissociate/associate from a particular site far away from the system boundaries. A theoretical analysis of the model is based on cluster mean-field approximation that allows for a comprehensive description of the stationary properties in the system. It is found that the topology and nature of stationary phase diagrams for varying association/dissociation rates strongly depend on the sign and strength of interactions. Extensive Monte Carlo simulations are implemented to test our theoretical predictions. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Ma, WeicaiZhang, PengZhao, XinXue, Leyang...
12页查看更多>>摘要：The outbreak of coronavirus disease 2019 (COVID-19) threatens the health and safety of all humanity. This disease has a prominent feature: the presymptomatic and asymptomatic viral carriers can spread the disease. It is crucial to estimate the impact of this undetected transmission on epidemic outbreaks. Currently, disease-related information has been widely disseminated by the mass media. To investigate the impact of both individuals and mass media information dissemination on the epidemic spreading, we establish a new UAU-SEIR (Unaware-Aware-Unaware-Susceptible-Exposed-Infected-Recovered) model with mass media on two-layer multiplex networks. In the model, E-state individuals denote asymptomatic infections, and a single node connecting to all individuals denotes the mass media. In this work, we use the Microscopic Markovian Chain Approach (MMCA) to derive the epidemic threshold. Comparing the MMCA theoretical results with Monte Carlo (MC) simulations, we find that the MMCA has a good consistency with MC simulations. In addition, we also analyze the impact of model parameters on epidemic spreading and epidemic threshold. The results show that reducing the proportion of asymptomatic infections, accelerating the dissemination of information between individuals and the dissemination of information via the mass media can effectively inhibit the epidemic spreading and raise the epidemic threshold. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Ribeiro, H. V.Lenzi, M. K.Lenzi, E. K.Guilherme, L. M. S....
8页查看更多>>摘要：We investigate a diffusion process in heterogeneous media where particles stochastically reset to their initial positions at a constant rate. The heterogeneous media is modeled using a spatial-dependent diffusion coefficient with a power-law dependence on particles' positions. We use the Green function approach to obtain exact solutions for the probability distribution of particles' positions and the mean square displacement. These results are further compared and agree with numerical simulations of a Langevin equation. We also study the first-passage time problem associated with this diffusion process and obtain an exact expression for the mean first-passage time. Our findings show that this system exhibits non-Gaussian distributions, transient anomalous diffusion (sub- or superdiffusion) and stationary states that simultaneously depend on the media heterogeneity and the resetting rate. We further demonstrate that the media heterogeneity non-trivially affect the mean first-passage time, yielding an optimal resetting rate for which this quantity displays a minimum. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
