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The dynamics of competitive information diffusion over a connected social network is investigated in this paper. A modified SIR model for two competitive information is presented, where each individual may turn to either of the two information after interacting with a spreader, while the spreader associated with one information may change into the other information. The population is divided into three subgroups: innovators, ordinary and laggard subgroups, respectively. It is assumed that individuals in different subgroups have different spreading rates and switching rates, when they interact with others. The influence of innovators and network topology on the dynamics of the competitive information diffusion is analyzed through numerous numerical simulations. It is observed that innovators and larger network degree can help enlarge the coverage of the information among the population, but they cannot help one information to compete with the other one. Moreover, innovators cannot always accelerate the convergence speed, which depends more on the network topology. • Competitive information diffusion among group-based population is investigated. • Innovator cannot help one information to compete with the other one. • The convergence speed depends more on the network topology than the proportion of innovators. [ABSTRACT FROM AUTHOR]

The dynamics of two-word naming game incorporating the influence of biased assimilation over adaptive network is investigated in this paper. Firstly an extended naming game with biased assimilation (NGBA) is proposed. The hearer in NGBA accepts the received information in a biased manner, where he may refuse to accept the conveyed word from the speaker with a predefined probability, if the conveyed word is different from his current memory. Secondly, the adaptive network is formulated by rewiring the links. Theoretical analysis is developed to show that the population in NGBA will eventually reach global consensus on either A or B. Numerical simulation results show that the larger strength of biased assimilation on both words, the slower convergence speed, while larger strength of biased assimilation on only one word can slightly accelerate the convergence; larger population size can make the rate of convergence slower to a large extent when it increases from a relatively small size, while such effect becomes minor when the population size is large; the behavior of adaptively reconnecting the existing links can greatly accelerate the rate of convergence especially on the sparse connected network. [ABSTRACT FROM AUTHOR]

In this paper, we study the dynamics of naming game where individuals are under the influence of memory loss. An extended naming game incorporating memory loss is proposed. Different from the existing naming game models, the individual in the proposed model would forget some words with a probability in his memory during interaction and keep his conveyed word unchanged until he reaches a local agreement. We analyze the dynamics of the proposed model through extensive and comprehensive simulations, where four typical networks with different configuration are employed. The influence of memory loss as well as the population size on the performance of the proposed model is investigated. The simulation results show that (i) the stronger memory loss, the larger convergence time; (ii) as the strength of memory loss becomes stronger, maximum number of total words will decrease, while the maximum number of different words among the population remains almost unchanged; (iii) the maximum number of different words increases linearly with the increase of the population size and coincides with each other under different strength of memory loss. The findings in the proposed model may give an insight to understand better the influence of memory loss on the transient dynamics of language evolution and opinion formation over networks. [ABSTRACT FROM AUTHOR]

Adaptive tracking control of a class of MIMO nonlinear system preceded by unknown hysteresis is investigated. Based on dynamic surface control, an adaptive robust control law is developed and compensators are designed to mitigate the influences of both the unknown bounded external uncertainties and the unknown Prandtl–Islinskii hysteresis. By adopting the low-pass filters, the explosion of complexity caused by tedious computation of the time derivatives of the virtual control laws is overcome. With the proposed control scheme, the closed-loop system is proved to be semi-globally ultimately bounded by the Lyapunov stability theory, and the output of the controlled system can track the desired trajectories with an arbitrarily small error. Finally, numerical simulations are given to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

An agent-based model incorporating biased assimilation is proposed in this paper to investigate opinion dynamics over a connected social network. The opinion of each agent is represented by a sequence of arguments, and it evolves through the interactions between agents. The probability that one agent chooses another to communicate depends on the similarity of their opinions. During every interaction, interacting agents exchange the argument randomly selected from the corresponding arguments sequence. Theoretical analysis reveals that this model results in consensus on either extreme positive opinion or extreme negative opinion, or generates bi-polarization. Numerical simulations are carried out to investigate the dynamics of the model over different networks. Results are obtained in terms of the effect of homophily, biased assimilation and network topology on opinion formation. [ABSTRACT FROM AUTHOR]

It is widely acknowledged that emerging stock markets and mature stock markets differ in many respects, while research on emerging markets is rather insufficient compared to well-studied mature markets. In this paper, we investigate the unique network properties and market pattern of the emerging Chinese stock market of crude oil related companies with the implementation of minimum spanning trees(MST) and hierarchical trees(HT). For a robust quantification of cross-correlation between stocks, detrended cross-correlation coefficient (DCCA coefficient) and partial cross-correlation coefficient (DPXA coefficient) are applied to reveal the overall cross-correlation and identify the common external factor on price fluctuations respectively. The paper depicts the dynamic evolution of Chinese stock market of crude oil related companies, showing the variation of structural density and stability under different time scales and market environments. The establishment of correlation-based networks provides a more pertinent explanation for the unusual market structure and conduct of the emerging Chinese stock market, contributing to both portfolio decisions and risk resistance. • Network properties and market patterns of the emerging Chinese stock market of crude oil related companies are explored by MST and HT. • DCCA coefficient and DPXA coefficient are applied respectively. • Small-volume stocks capture hub positions more frequently in both upturn and recession periods. [ABSTRACT FROM AUTHOR]