Opinion reversion is an important phenomenon in network emergencies and hot events. In order to explore the internal mechanism and the evolution law of public opinion reversion, we propose an opinion evolution model based on opinion leaders from the perspective of opinion dynamics. The model is applied to simulate the evolution process of public opinion under the guidance of opinion leaders. We take opinion leaders into account, improve the HK model and use social network analysis method to identify the opinion leaders based on scale-free network structure. To verify the simulation results we select the reversal case on sina twitter. The results show that opinion leaders play a key role in the evolution of opinion, which can guide the public opinion to reverse. The results also indicate that the improved model can simulate the evolution process of network opinion reversion.

This paper introduces the derivation of basic reproduction numbers for several epidemic models. The basic reproduction number plays an important role in describing the dynamic behavior of infectious disease models,which is an important indicator to determine the prevalence of diseases. Therefore, the basic reproductive number is a significant reference for the prevention and control of diseases and the immunization strategy. The basic reproduction numbers can be derived by means of the definition, the monotonicity of the infected individuals at the initial moment, the existence of the positive equilibrium and the local stability of the disease-free equilibrium, the numerical simulation,respectively. This paper introduces many epidemic models especially network model and calculates their basic reproduction numbers. Finally, we analyze the changes in the basic reproduction numbers during different periods.

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

The conditions for the spontaneous emergence of cooperation under the complex human behavior model have become the focus of many disciplines. Exploring the conditions for cooperation has both important scientific significance and theoretical value for understanding the institutional arrangements in human society. The social dilemma games provide a theoretical prototype for studying cooperation issues between multiple individuals. As a dynamic analysis method that can describe individuals′ learning and strategy adjustment processes, evolutionary game theory has been one of the most effective frameworks for studying the evolution of cooperation. This review article systematically summarizes the research progress of using the evolutionary game method to study the issues of group cooperation in social dilemma games. Specifically, the following topics are included: research progress of (1) social dilemma game models, evolutionary game theory and equilibrium analysis methods, (2) social dilemma games and the evolution of cooperation under reward/punishment mechanism and reputation mechanism, (3) social dilemma games and the evolution of cooperation with separation strategy and extortion strategy, and (4) social dilemma games and the evolution of cooperation under the network reciprocity. Finally, prospects for further research issues in this area are presented.

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.

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.

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.

Evolutionary games on complex networks is a new interdisciplinary research field at the cross-point of complex networks and evolutionary game. With a complex network and and an evolutionary game dynamics representing the interaction structure among agents and the decision paradigm respectively, evolutionary games on complex networks provides a systematic framework for analyzing and predicting the collective decision-making behaviors of complex interactive populations. This review aims to give a brief survey of evolutionary game dynamics on complex networks from a computational perspective. In detail, we will firstly present a mathematical formulation of the model of evolutionary game dynamics on complex networks, and then analyze the computational complexity of these networked game dynamics, and finally outline some main analytical results about evolutionary game dynamics on complex networks. This computational survey will be a well complement to those simulation results in evolutionary game dynamics on complex networks.

The optimization of platform regulation strategy is the key to ensure the compliance and legality of suppliers and anchors in live broadcast e-commerce. Aiming at the interest relationship, behavior strategy and game relationship of suppliers, live broadcasting platform and anchor, this paper constructs the behavior strategy evolution game model of the three, reveals the behavior strategy evolution law of live broadcasting e-commerce under the platform regulation, and uses Netlogo to simulate the strategy evolution process. The research shows that the appropriate punishment and restraint of the platform is helpful to standardize the behavior of suppliers and anchors; Higher platform subsidies can encourage suppliers and anchors to standardize their behavior, but at the expense of their own interests, the system is difficult to stabilize; The illegal income factor is an important factor affecting the supplier's supply strategy and anchor behavior choice. Accordingly, the basic strategies and specific measures to standardize the behavior of suppliers and anchors are put forward.

The outbreak of COVID19 has turned many areas into disaster areas. In order to provide timely relief to the disaster areas, accurate supply of post-disaster emergency resources has become the primary factor to ensure the safety of the people in the disaster areas. In this paper, SEIR was used to predict the number of infected people in each disaster area at the decision-making moment, and then the weight of urgency degree and material demand in the disaster area were calculated. Based on the degree of urgency, a multi-objective optimization model of emergency resource scheduling was constructed to maximize the satisfaction of the victims, minimize the total cost and consider the fairness of distribution. A multi-objective artificial bee colony algorithm is proposed. Aiming at the disadvantages of artificial bee colony algorithm such as precocity, the dynamic parameter and Pareto solution set are used to define the new bee colony location updating formula, and the teaching optimization is used to disturb the bee colony location, so as to avoid the algorithm falling into local extremum. The simulation results show that the proposed model and algorithm can effectively solve the problem of optimal allocation of emergency resources at multiple disaster points under epidemic events, and the improved algorithm has better performance.