Aiming at the problem that the traditional propagation model is difficult to accurately describe the law of information propagation in the blockchain social network, based on the information propagation characteristics of blockchain social networks, this paper considers the influence of the incentive mechanism of blockchain social networks on information propagation, uses evolutionary games to define the state transition probability and proposes a new blockchain social network information propagation model. The influence of group density, state transition probability, and incentive policies on information propagation is analyzed through simulation experiments. Experimental results show that the model can accurately describe the law of information propagation in blockchain social networks. The model proposed in this paper can effectively restrain the propagation of low-quality information in social networks and further build a good network public opinion environment.

The new employment form has broken the traditional employment mode. By using the evolutionary game idea and introducing laborer supervision, the paper constructs the evolutionary game model of the relationship between platform organization and platform enterprise, and carries out the simulation analysis. The evolution results show that laborer supervision has a significant impact on the compliance behavior of platform enterprises and the choice of control strategies of platform organization. And strengthening laborer supervision is more conducive to platform organization to strengthen supervision and improve the code of conduct of platform enterprises. Platform organization should give full play to the laborer's regulatory utility in the cooperative governance of network platform employment to realize the mutual trust and harmonious platform organization-platform enterprise relationship and promote the effective employment of laborers.

In order to explore the enabling mechanism, as well as the mechanism of government classification regulation on smart platforms and pharmaceutical enterprises' CSR strategies, a tripartite evolutionary game model for the government, smart platforms, and pharmaceutical enterprise is constructed. Through the analysis of the critical condition, stability and evolution path of the evolution of the tripartite strategy, it reveals the action mechanism of each factor and the evolution law of the tripartite game, and analyzes the influence of key factors such as the equilibrium state of system evolution and regulatory policies based on Anylogic simulation. The research shows that: Government classification regulations significantly affect the CSR performance decision and intelligent supply chain construction of pharmaceutical enterprises and intelligent platform, and the regulatory effect is affected by the sensitivity of the policies of both parties; Increased intelligence can strengthen the risk flexibility and autonomy of intelligent platform and pharmaceutical enterprises, but there are limitations in the space and time for pharmaceutical enterprises to implement strategies; When the reward coefficient is moderate, the evolution speed of smart platforms and pharmaceutical enterprise is significantly positively correlated with the reward coefficient. Based on this, it proposes measures to improve the efficiency of government CSR governance and solve the dilemma of CSR implementation in the pharmaceutical industry.

In order to explore the impact of network structure and knowledge base on enterprise innovation performance, the cooperative innovation network of industry-university-research institute is constructed from the perspective of social network, and the negative binomial regression model is used for empirical analysis. The results show that the industry-university-research innovation network centrality has a significant positive impact on enterprise innovation performance, network structure hole does not show a significant inverted U-shaped relationship with enterprise innovation performance, knowledge base has a significant positive impact on enterprise innovation performance, and the interaction between knowledge base and network centrality has a significant negative impact on enterprise innovation performance, the interaction between knowledge base and structure hole has a positive effect on enterprise innovation performance, but it is not significant.

Cooperative behavior is widespread, and how to promote the emergence of cooperative behavior has been a hot issue of systems science. Combining the traditional complex network model with the game theory model, this paper proposes a grouping selection rule for individual interaction and investigates the joint influence of the parameter dividing the groups, the individual's memory length, and the network clustering coefficient on cooperative behavior. Through simulation experiments, it is found that the grouping selection can effectively promote not only the emergence of cooperative behaviors in adjustable clustering networks, but also the individuals' cooperation in lattice and small-world networks, indicating that the rule has certain universality and repeatability. It provides a new approach to improve the overall level of cooperation in large-scale groups, enabling us to gain a deeper understanding of real-world cooperation phenomena and opening up a new avenue for exploring the reasons behind individuals' cooperative behavior.

The Boolean control networks with multiple channels are investigated in this paper. The theory of semi-tensor product of matrices is applied to model Boolean control networks with outputting through multiple channels, and to study its observability. Firstly, by using of the semi-tensor product of matrices, the algebraic expression of this model is obtained; Secondly, based on the algebraic expression, the sufficient and necessary conditions for observability of Boolean control networks are proved under free control sequence and state feedback control; Finally, an algorithm is designed to construct the feedback control matrix which makes the system observable. An example is given to illustrate the validity of the results.

In order to make full use of emergency supplies and improve emergency response capacity, a multi-objective optimization model was established to minimize the average waiting time of disaster sites and the dispatching cost of emergency supplies, taking into account the constraints of road passage and transportation capacity in the initial stage of disaster. Population entropy and Gaussian function were introduced by the improved NSGA-II, based on adaptive mechanism, to dynamically adjust mutation and crossover probability, and combined mutation and crossover process with horizontal and vertical information of evolution to guide population evolution and improve evolution speed. A random variable neighborhood search operator based on greedy idea was designed to fully explore the solution space. The influence of the same on evolution in Pareto frontier was eliminated by adjusting the strategy. An example was given to verify the proposed algorithm, and the results shown that the proposed algorithm was superior to the traditional NSGA-II algorithm and the known literature algorithm, and maintained good convergence and obtained better diversity.

To study the controllability of a class of directed signed multi-agent systems based on directed paths, the Laplacian matrix and graph theory are used to analyze. Firstly, it is proved that adding or removing a specific class of edges in the network does not change the system's controllability. Secondly, the controllability of the directed paths is studied, and then we check the system's controllability after adding reverse or forward edges in the directed paths. The results show that increasing the reverse edges in directed paths has no influences on its original controllability, However, the system's controllability needs to be analyzed according to the specific situations while increasing the forward ones by taking advantage of the almost equitable partitons. Finally, combined with the results above, a method of constructing directed complex network topologies is given.

In order to identify the spatio-temporal characteristics of the traffic of communication system, Network model and core-periphery model is used to analyze the traffic data. Considering both the temporal information and the spatial information, we construct a data-voice coupled bipartite network. The edge weight distribution of the temporal network is lognormal. The maximal connected subgraph of the spatial network is denoted as S-network and it is a small world network. The edge weight of the S-network obeys power-law distribution. The frequency of coupling events in core is higher than that in periphery. The core-periphery model can effectively identifies spatio-temporal areas where data-voice coupling events occur intensively.

Novel coronavirus is a new type of virus, and its transmission characteristics are different from previous virus. Infected people not only have an incubation period, but also a large number of asymptomatic infections. Based on the classic model SEIR, this study redefines the latent state as close contact state, introduces an asymptomatic state of infection, and the influence of time on the state transition parameters in the model is considered, proposed a new transmission model which includes five types of states: susceptible state, close contact state, asymptomatic infection state, infected state, and removed state. The model uses the actual epidemic data of Hubei Province to conduct experiments, and uses RMSE and MAPE as evaluation indicators to compare the experimental results. The results show that the fitting accuracy of the SCUIR model has been significantly improved. Compared with the traditional model, the fitting error is reduced by 8.3%~47.6%, and hidden data that is difficult to count in the epidemic can be calculated, which further characterizes the mechanism of epidemic transmission.

The subsidy of innovation voucher policy for low-carbon technological innovation of manufacturing enterprises has the feature of post-subsidy. This paper constructs a game model of government regulation and manufacturing enterprises to carry out low-carbon technology innovation under the policy of innovative vouchers subsidy. The results show that, first, when government supervision is more than a certain threshold, it can offset the insufficient of innovation motivation in the manufacturing enterprises. Second, government should choose different guidance method according to the innovation risk level: in high-risk situations, the impact of increasing punishment on the evolution speed of enterprises is more significant, thus, the government should promote the evolution of manufacturing enterprises to low-carbon technology innovation strategies through punishment mechanisms; in low-risk situations, the effect of punishment on the evolution speed of manufacturing enterprises tends to be steady, and the increase of subsidy coefficient can speed up their evolution. At this time, the government should take the subsidy mechanism as the mainstay and the penalty mechanism as the supplement to guide manufacturing enterprises to carry out low-carbon technological innovation.

In order to study the relevant policies under the evolution of scrap copper trade, this paper analyzes the evolution characteristics of scrap copper trade based on complex network theory, and analyzes its correlation with coal index, global copper reserves, global copper output and copper futures price. The results show that there is a positive and negative correlation between coal index, copper futures price and network statistical characteristics in the same period or the previous period. Global copper production, reserves and network statistical characteristics show positive and negative correlation in the same period. Based on the above results, this paper puts forward the corresponding response strategies from the aspects of import structure, energy price adjustment and import policy.