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This paper explores the law of emotion transmission on the social Internet. By applying higher-order network theory, ER random networks are reconstructed and higher-order network structures with multiple interaction points are generated. The I-state of the SI model is extended to set up the propagation rules of emotions based on higher-order networks. Concepts such as sentiment recession rate are introduced to make sentiment propagate through different simplexes, so as to obtain the propagation rules of higher-order networks. Qualitative analysis of emotional changes during the propagation process verifies the phase transition phenomenon of emotions in the network. By comparing simulated results with actual data, we demonstrate the accuracy of our higher-order network-based emotion propagation model in reflecting real-world trends and outcomes. The study also explores the effects of factors such as the initial proportion of extreme emotions on emotion propagation. This study provides valuable insights into understanding the operation of complex systems and offers important implications for government predictions of opinion development. [ABSTRACT FROM AUTHOR]

The complexity of systems stems from the richness of the group interactions among their units. Classical networks exhibit identified limits in the study of complex systems, where links connect pairs of nodes, inability to comprehensively describe higher-order interactions in networks. Higher-order networks can enhance modeling capacities of group interaction networks and help understand and predict network dynamical behavior. This paper constructs a social hypernetwork with a group structure by analyzing a community overlapping structure and a network iterative relationship, and the overlapping relationship between communities is logically separated. Considering the different group behavior pattern and attention focus, we defined the group cognitive disparity, group credibility, group cohesion index, hyperedge strength to study the relationship between information dissemination and network evolution. This study shows that groups can alter the connected network through information propagation, and users in social networks tend to form highly connected groups or communities in information dissemination. Propagation networks with high clustering coefficients promote the fractal information dissemination, which in itself drives the fractal evolution of groups within the network. This study emphasizes the significant role of "key groups" with overlapping structures among communities in group network propagation. Real cases provide evidence for the clustering phenomenon and fractal evolution of networks. [ABSTRACT FROM AUTHOR]

This paper focuses on the internal law of online public opinion spreading under the conditions of different types of spreaders by constructing the model of public opinion spreading by subtypes of spreaders. The new S I S I O I N model is constructed by abstracting factors such as netizens' interests, emotions and views on hot events in the Internet into propensity indices. The simulation results show that the spread of public opinion after categorizing netizens is more in line with the spread of public opinion in reality. Moreover, netizens tend to show different spreading behaviors in different public opinion cases, and the degree of change in netizens' propensity varies greatly. All the data used in the simulation experiments are real data and analyzed with real events. The research results are helpful for what kind of control strategies are adopted after the occurrence of hotspot events. [ABSTRACT FROM AUTHOR]

In real network systems, there are numerous information propagation phenomena. However, the impact of homogeneity on information propagation is still not well understood. In this paper, we introduce a dynamic information propagation model named H-SIR. We also incorporate the propagation mechanism of content homogeneity and define the dynamic propagation rate based on users' interest locking and fatigue level. Through simulation experiments, we demonstrate how homogeneous information spreads and evolves on the network. Furthermore, we compare the model with real data of COVID-19 information on the Sina-Weibo social platform and the traditional SIR model to jointly validate our model. Our results show that in social networks, if the themes of homogeneous information are diverse, even with an increasing amount of homogeneous information, it will hardly affect the propagation of this type of information. However, if users are recommended excessive homogeneous information, they will not focus entirely but instead display selective contact behavior. In the case of information fatigue, a type of information with a low degree of homogeneity will survive longer than a type of information with a high degree of homogeneity. Finally, we propose corresponding strategies to alleviate the phenomenon of information homogenization in social networks. [ABSTRACT FROM AUTHOR]

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 DB${D}_B$ 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 DB${D}_B$. 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 DB${D}_B$ 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 DB${D}_B$, 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. [ABSTRACT FROM AUTHOR]

The industry is the key to economic development. This paper proposed a fractal evolutionary dynamic model of industry based on the propagation dynamics theory from the perspective of fractal theory. The model quantitatively portrays the evolutionary trend of industries and provides reasonable solutions to the problems faced by industrial structure optimization. First, the fractal characteristics of the industrial system are analyzed, and the proposed model is used to study the evolution of industry and enterprise impact values at different granularities. Subsequently, the impact of micro firms on the macro industrial development was further investigated. Finally, the correctness of the model simulation results was verified by real data. The results showed that the evolution of industries under different granularities shows self-similarity and that micro industries with small and medium-sized enterprises (SMEs) were more influential in the development of industries. Finally, the proposed fractal evolutionary dynamics model of the industry has proven to be reasonable and effective through empirical comparison. This study provided new insights for solving industrial development problems with high theoreticality and reliability. [ABSTRACT FROM AUTHOR]

In the research of network structure, long ties are considered to be a hidden but valuable interaction. In this paper, we innovatively interpret the long ties structure in traditional research as a higher-order information spreading path. It effectively avoids the homogenization of information level and redundancy of network structure. The spreading bridge model based on high-order long ties structure is established accordingly. Modern online social behavior is modeled by establishing spreading expansion rate and spreading diffusion rate to improve model adaptability. The spread dynamics model based on multivariate interactions is developed by combining direct-linked spread as a binary interaction relationship in traditional research with bridge-linked spread. The validity of the model is verified by comparing the model simulation results with real cases. [ABSTRACT FROM AUTHOR]

This paper investigates the factors influencing the spread of popular topic competition from the perspective of propagation dynamics. The popularity of a topic is directly influenced by the level of people's interest in it. The rarity, deviation and type can influence the level of interest in such topics. In addition, the topic publisher also has an impact on the competitive dissemination of the topic. The higher the rank of the original publisher is, the easier it spreads. Compared with small bloggers without fans, it is easier to attract people's attention, and the heat of the topic will rise faster. The results show that the rarer the topic, the greater the deviation from reality, and the more followers the originator has, the more competitive it is. [ABSTRACT FROM AUTHOR]