This paper uses the AR (1)-GJR (1, 1)-SKT model to describe the marginal distribution of 15 stock index returns. A hybrid R-Vine Copula model is constructed to analyze the risk contagion effect of international stock markets under the four crisis events, which include the subprime mortgage crisis, the European debt crisis, the abnormal fluctuations of the Chinese stock market in 2015 and the Sino-US trade friction in 2018. The empirical results show that the international stock markets maintain symmetrical top-to-bottom dependence characteristics in the long term. The risk contagion will cause the Kendall rank correlation coefficient and tail correlation coefficient among stock markets to rise suddenly. The subprime crisis has a strong contagious effect and a long duration, and the European debt crisis is relatively mild. In 2015, the abnormal fluctuations in the Chinese stock market had a strong contagious effect on international stock markets, but the duration was short. In 2018, the Sino-US trade friction held a weaker contagious effect on the international stock markets. China's Shanghai and Shenzhen stock markets are more integrated with Hong Kong stock market in China.

As a type of collective behavior in social systems, opinion polarization may greatly influence social stability. Thus, this paper systematically sorts out and summarizes the research status of opinion polarization. Based on reviews of the concept and modeling of opinion polarization in social and political fields, the two interaction mechanisms of opinions that lead to opinion polarization are extracted. From the perspective of individuals, the paper focuses on discussion two kinds of social psychological effects that may lead to opinion polarization, viz., biased assimilation and hostile media effect. The key to integration of polarization research in different fields lies in the internal change mechanism of individual opinion. One of the emphasis in future research should focus on the mutual corroboration of models and real data.

To improve the ability to predict the evolution trend of public opinion, a public opinion evolution trend prediction model based on an improved wolf pack algorithm and optimized long-short term memory neural network is proposed. Use Halton Sequence to initialization to improve population diversity. Design step factor to perform Gauss-Sine perturbation transformation to improve wolf group exploration and development capabilities. Combine with the spiral in the whale optimization algorithm to improve the siege mechanism to enhance the local search ability of wolves. The bidirectional memory population is used to increase the cooperative ability of the wolf pack. The improved wolf pack algorithm (IWPA) is applied to the hyperparameter prediction of the LSTM neural network. Using keywords such as “COVID-19” and “Food Safety”, the experiment proves that the IWPA-LSTM neural network public opinion evolution prediction model has good accuracy and generality. The model is suitable for the prediction of various public opinion evolution trends.

In the majority of cases, networks are beneficial. However, many times it may also be harmful, such as terrorist networks and disease spreading networks. It has become an urgent challenging problem to disintegrate these harmful networks by various methods such as immunization, block, isolation, disturbance, and attack. The core task of network disintegration is to identify the “critical nodes (edges)”. This survey firstly gives the mathematical description of network disintegration. On this basis, this survey then reviews the status of network disintegration study in the fields of operations research, network science, and computer science based on mathematical programming, the centrality metrics, the heuristic algorithms, evolutionary computation, and machine learning, respectively. Lastly, this survey presents the prospects of network disintegration study from the aspects of the target network, disintegration model, and algorithm.

The enhancement of the robustness of complex networks has been a hotspot in the field of network science in recent years. It is both of great scientific significance and theoretical value to explore the strategy of enhancing the robustness for network structure design and function improvement. On the basis of extensive collation and systematic analysis of domestic and foreign literature, this paper summarizes comprehensively the key point and main ideasof the current research on the enhancement strategies of complex networks robustness from three aspects: pre-defense, in-process recovery and post-optimization. The advantages, disadvantages and applicability of different strategies are compared and analyzed. Then we look forward to future research direction in this field.

The topological characteristics and resilience analysis of public transportation systems are of great significance in urban management to ensure its safe and sustainable operation. This paper constructs a bus-metro interdependent network model based on the passenger transfer relationship and uses deep learning to identify their network topology attributes. A comprehensive importance indicator of the nodes is established by entropy weight-technique for order preference by similarity to ideal solution (EWM-TOPSIS), and the resilience of the network under different recovery strategies are analyzed. In order to verify the applicability and accuracy of the method, this study takes Wuhan's public transportation network as an example, which has practical guiding significance for the post-disaster recovery and operation management of the urban public transportation system.

In order to solve the problem of insufficient consideration of social class in the existing opinion dynamics studies, an opinion dynamics model based on the Hegselmann and Krause (HK) model is proposed by combining the topological properties of nodes in the network with the social structure on the basis of the heterogeneity of individual influences. In three types of classic complex network and an empirical network, three social structures are constructed with the social class division method adopted from the field of sociology according to capital. Simulation experiments demonstrate that the social structure has a significant impact on opinion evolution. Adjustment of the social structure through social policies is conducive to the consensus of opinions. To study the inconsistency of opinion evolution in the same type of social structure in the actual society, different initial opinion distributions of upper-class individuals are set up in the type-B pyramidal and olive-shaped social structures. It is inferred that the polarization and unification of upper-class individuals’ opinions will influence the polarization and unification of public opinion correspondingly.

As a form of public opinion, view and attitude, opinion widely exists in people′s life. It is important to clarify the evolution mechanism of opinion, explicit the existing research progress, and promote the rational governance of public opinion. In view of the lack of relevant introduction of binary opinion dynamics and the separation of the relationship between binary and group opinion dynamics, this paper summarized the research status of opinion dynamics at home and abroad. Firstly, it introduces the binary opinion dynamics model from the perspective of research method and interaction characteristic; Secondly, it combed the research results of group opinion dynamics from the perspective of individual characteristic, behavior characteristic, opinion characteristic, external environment and perspective dynamics. Finally, based on the existing research, it clarifies the problems, the mechanism of opinion evolution from the empirical perspective, the strengthening of opinions and the reduction of disputes, and the relationship between the evolution of views and group decision-making, that need to be solved in the future.

Complex networks nodes can have multiple attributes, and different attributes or attribute sets will lead to different connections between nodes, thus the network would have different functions.Aiming at the problem that existing complex network models can not construct networks with different functions according to the selected attributes of nodes, a multifunctional complex network model is proposed.The network model is represented only by nodes and their attribute sets. Different network topology and network functions are determined by selecting the attributes of nodes and defining the mapping rules of nodes under the corresponding attributes.By establishing and analyzing a missile defense network, the availability and effectiveness of the multi-functional complex network model are verified

In order to promote the development of autonomous driving technology, this paper focuses on the lane-changing decision-making behavior of autonomous vehicles. First, the lane-changing intention is quantified objectively, and then the lane-changing collision probability and the lane-changing dynamic risky coefficient are introduced. Based on the game theory, the decision-making behavior model of the lane-changing game for autonomous vehicles is established. Besides, speed gains are considered as the objective of game gains. Therefore, autonomous vehicles can change lanes in a coordinated, safe and reasonable manner. Finally, with SUMO software, the traditional LC2013 lane-changing model and the decision-making behavior model of the lane-changing game are used for simulation experiments and comparative analysis. The results show that the decision-making behavior model of the lane-changing game has higher stability, reliability, safety and lane utilization.