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.

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.

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.

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 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.

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 this paper, we use generative adversarial network (GAN) to improve semantic segmentation of images. The model is composed of a semantic segmentation network and a discriminant network, where the segmentation network responses for generating semantic segmentation result while the discriminant network responses for detecting the difference between the generated result and the labels on the global structure level and improving the segmentation effect. In order to extract context information, we adopt the spatial pyramid pooling module in the segmentation network, which could perform pooling operation on multiple levels of sub-regions. Meanwhile, in order to solve the problem of a large number of manual annotations needed in the semantic segmentation data set, we use the discriminant network to generate pseudo labels and realize semi-supervision in the training of the segmentation network. The model has been tested using PASCAL VOC2012 dataset, and the results show that supervised and semi-supervised approaches proposed in this paper are superior to the existing methods.

This paper investigated the evolution of cooperation in three typical social network topologies: regular lattice network, scale-free network and small-world network, using NetLogo simulation platform and ABM method to design simulation experiment. We also studied which network is more conducive to the occurrence of cooperation. By introducing factors such as network scale, initial cooperation probability, betrayal benefit, selection mode of neighbor node, interaction rules and so on, this paper measured how the above variables affect the occurrence and continuity of cooperation. Then we compared the different evolution results and discussed how to design effective incentive mechanism to maintain and to promote cooperation. Experimental results show that regular lattice network and small-world network have more commonality, while scale-free network is more conducive to the evolution of cooperation. In order to develop an effective incentive mechanism to promote the occurrence and continuity of cooperation, the design of incentive mechanism should take full account of the influence of social network structure of groups.

In link prediction, the similarity indices based on random walk process often set the probability of particles transferring to adjacent nodes to be equal, but neglecting the influence of node degree on the transition probability. To save this problem, a link prediction algorithm of biased random walk with restart is proposed. Firstly, we redefine the transfer probability of particles by referring to biased random walk. Then we apply it to the random walk with restart to explore the effect of node degree on the transfer of particles. Finally, on the basis of biased random walk,the proposed index is compared with six classical similarity indices.The experimental results of six real data sets show that the prediction algorithm of biased random walk with restart has higher prediction accuracy than that the unbiased one, and is better than other similarity indices.