In order to study the universality of the interactions among different markets, we analyze the cross-correlation matrix of the price of the Chinese and American bank stocks. We then find that the stock prices of the emerging market are more correlated than that of the developed market. Considering that the values of the components for the eigenvector may be positive or negative, we analyze the differences between two markets in combination with the endogenous and exogenous events which influence the financial markets. We find that the sparse pattern of components of eigenvectors out of the threshold value has no change in American bank stocks before and after the subprime crisis. However, it changes from sparse to dense for Chinese bank stocks. By using the threshold value to exclude the external factors, we simulate the interactions in financial markets.
Innovation, which involves technological transformation and management reorganization, brings about significant changes in modern society. In this paper, to investigate how innovations can be promoted, we propose a game-based model to study the co-evolutionary dynamics of human innovative behaviors. A simulation on scale-free networks is conducted, in which the innovative behavior of each node is determined and updated based on the feedback regarding its innovation, namely the diffusion of the innovation status. Numerical simulations of the model generate a series of patterns, which is consistent with people＇s daily experiences and perceptions as regards real-world innovative behaviors. Specifically, various scaling spatiotemporal properties and rich structural impacts on dynamics can be observed. This model provides a novel approach to understand the evolution of innovative behaviors and provides insight for strategy studies of innovation promotion.
Ying-Ting LinXiao-Pu HanBo-Kui ChenJun ZhouBing-Hong Wang
We ascertain the modularity-like objective function whose optimization is equivalent to the maximum likelihood in annotated networks. We demonstrate that the modularity-like objective function is a lin- ear combination of modularity and conditional entropy. In contrast with statistical inference methods, in our method, the influence of the metadata is adjustable; when its influence is strong enough, the metadata can be recovered. Conversely, when it is weak, the detection may correspond to another partition. Between the two, there is a transition. This paper provides a concept for expanding the scope of modularity methods.
An extended social force model with a dynamic navigation field is proposed to study bidirectional pedestrian movement. The dynamic navigation field is introduced to describe the desired direction of pedestrian motion resulting from the decision-making processes of pedestrians. The macroscopic fundamental diagrams obtained using the extended model are validated against camera-based observations. Numerical results show that this extended model can reproduce collective phenomena in pedestrian traffic, such as dynamic multilane flow and stable separate-lane flow. Pedestrians＇ path choice behavior significantly affects the probability of congestion and the number of self-organized lanes.
Yan-Qun JiangBo-Kui ChenBing-Hong WangWeng-Fai WongBing-Yang Cao
