Research on the Influence of Information Iterative Propagation on Complex Network Structure.

Dynamic propagation will affect the change of network structure. Different networks are affected by the iterative propagation of information to different degrees. The iterative propagation of information in the network changes the connection strength of the chain edge between nodes. Most studies on temporal networks build networks based on time characteristics, and the iterative propagation of information in the network can also reflect the time characteristics of network evolution. The change of network structure is a macromanifestation of time characteristics, whereas the dynamics in the network is a micromanifestation of time characteristics. How to concretely visualize the change of network structure influenced by the characteristics of propagation dynamics has become the focus of this article. The appearance of chain edge is the micro change of network structure, and the division of community is the macro change of network structure. Based on this, the node participation is proposed to quantify the influence of different users on the information propagation in the network, and it is simulated in different types of networks. By analyzing the iterative propagation of information, the weighted network of different networks based on the iterative propagation of information is constructed. Finally, the chain edge and community division in the network are analyzed to achieve the purpose of quantifying the influence of network propagation on complex network structure.

Epidemic propagation risk study with effective fractal dimension.

In this article, the risk of epidemic transmission on complex networks is studied from the perspective of effective fractal dimension. First, we introduce the method of calculating the effective fractal dimension D B ${D}_B$ of the network by taking a scale-free network as an example. Second, we propose the construction method of administrative fractal network and calculate the D B ${D}_B$ . using the classical susceptible exposed infectious removed (SEIR) infectious disease model, we simulate the virus propagation process on the administrative fractal network. The results show that the larger the D B ${D}_B$ is, the higher the risk of virus transmission is. Later, we proposed five parameters P, M, B, F, and D, where P denotes population mobility, M denotes geographical distance, B denotes GDP, F denotes D B ${D}_B$ , and D denotes population density. The new epidemic growth index formula I = ( P + ( 1 - M ) + B ) ( F + D ) $I = {( {P + ( {1 - M} ) + B} )}^{( {F + D} )}$ was obtained by combining these five parameters, and the validity of I in epidemic transmission risk assessment was demonstrated by parameter sensitivity analysis and reliability analysis. Finally, we also confirmed the reliability of the SEIR dynamic transmission model in simulating early COVID-19 transmission trends and the ability of timely quarantine measures to effectively control the spread of the epidemic.

Identification of Potential Cooperation Relationships Among Scientists.

In this article, the phenomenon of scientist cooperation in the scientist cooperation network is studied from the perspectives of information spread and link prediction. By mining the information in the scientist cooperation network, analyzing the cooperation has been generated and discovering potential cooperation opportunities. It helps to build a richer cooperation network with more content. Information spread can reflect the inner laws of network structure formation, and the link prediction method can retain the integrity of network information to the maximum extent. First, the real network is abstracted by analyzing its structure as well as node attributes into a simulated network. Second, the process of information spread in the cooperation network is simulated by improving the traditional SIS model. Some improvements are made to the link prediction algorithm for the impact brought to the network by information spread. Finally, the experimental results in the scientist cooperation network show that the hybrid weighted link prediction algorithm combining node attributes and spread factors can improve the accuracy of link prediction and provide suggestions for scientists to find partners. The comparative experiments on simulated and real networks not only validate the effectiveness of the propagation model in the scientist cooperation network, but also verify the accuracy of the hybrid weighted link prediction algorithm.

COVID-19 Propagation Model Based on Economic Development and Interventions.

In order to understand the influencing factors affecting the COVID-19 propagation, and analyze the development trend of the epidemic situation in the world, COVID-19 propagation model to simulate the COVID-19 propagation in the population is proposed in this paper. First of all, this paper analyzes the economic factors and interventions affecting the COVID-19 propagation in various different countries. Then, the touch number for COVID-19 High-risk Population Dynamic Network in this paper was redefined, and it predicts and analyzes the development trend of the epidemic situation in different countries. The simulation data and the published confirmed data by the world health organization could fit well, which also verified the reliability of the model. Finally, this paper also analyzes the impact of public awareness of prevention on the control of the epidemic. The analysis shows that increasing the awareness of prevention, timely and early adoption of protective measures such as wearing masks, and reducing travel can greatly reduce the risk of infection and the outbreak scale.

A two-step high-risk immunization based on high-risk immunization.

In this paper, the two-step high-risk immunization was investigated based on high-risk immunization for the SIRS model in small-world networks and scale-free networks. First, the effects of various immunization schemes are studied and compared. When the number of immune is same, the research result shows that the immune effect of the two-step high-risk immunization strategy is not the best nor the worst. However, the practicability is better compare with others. Furthermore, by changing the proportional of immunization the optimal immune effect can be achieved in the two-step high-risk immunization. Computation results verify that the two-step high-risk immunization is effective, and it is economic and feasible in practice.

Optimal pinning synchronization on directed complex network.

In this paper, pinning synchronization on directed network was considered. By analyzing, some general synchronization criteria on directed network were established. And then, we verified it on directed globally coupled network, directed scale-free network, and directed small-world network, respectively. The pinning nodes were selected, respectively, according to order of in-degrees and out-degrees. Through comparing and analyzing simulations, the optimal pinning scheme was found, and a practical principle was induced finally.

Efficient immunization strategies on complex networks.

In this paper, we proposed an efficient immunization-"high-risk immunization". The standard SIRS model was modified, respectively, on WS small-world network and BA scale-free network. Based on our new SIRS model, the density of infected individuals was analyzed from a theoretical point of view, and computer simulation was implemented on different networks. The results indicate that the high-risk immunization is effective, and it is economic and feasible in practice.