Abstract
In fact, information is not transmitted in a single message, but rather multiple messages are transmitted simultaneously and interact with each other as they are transmitted. Based on this phenomenon, a multi-information overlay network model that differs from the traditional network structure is first constructed on the basis of two classical network models, BA scale-free and WS Small World. When multiple messages are transmitted simultaneously, the competitive nature of the message affects the transmission process to some extent, and therefore a sense of competition for the message is introduced into the classical SIS model of infectious disease. Simulation experiments have shown that a sense of competition enhances or inhibits the spread of information to varying degrees, and that information with a high sense of competition has a greater scale of spread, in line with the real-life “Matthew effect”. In addition, the sense of competition also accelerates the spread of information. Therefore, it is possible to inhibit the spread of negative information by controlling the sense of competition, so as to achieve timely warning and control of negative information.
Highlights
With the diversification and complexity of information dissemination, people are often in a complex network of multiple information networks, and each network contains multiple individuals, and each individual is exposed to multiple information networks at the same time, for example, people are in real social networks and virtual social networks, and real social networks can be divided into various relationship circles such as family, friends and colleagues, and virtual social networks are divided into Facebook, Youtube, Twitter and Wechat [1]–[3], and each social network in a complex network has different competitiveness for different user groups
In the field of complex networks, the SIS and SIR models, which evolved from the infectious disease transmission model, have been discussed and studied by many scholars, and the models allow scholars to better understand the information dissemination problems that exist in many interdisciplinary disciplines [9]–[11]
Complex network theory has been widely applied to public opinion control, rumor propagation, advertising effects, social networks, and other areas [12]–[14].Among them, many have studied the results of information dissemination in the field of disease prevention and control [15]–[17], and some have discussed information prediction models based on online social network information dissemination
Summary
With the diversification and complexity of information dissemination, people are often in a complex network of multiple information networks, and each network contains multiple individuals, and each individual is exposed to multiple information networks at the same time, for example, people are in real social networks and virtual social networks, and real social networks can be divided into various relationship circles such as family, friends and colleagues, and virtual social networks are divided into Facebook, Youtube, Twitter and Wechat [1]–[3], and each social network in a complex network has different competitiveness for different user groups. In the field of complex networks, the SIS and SIR models, which evolved from the infectious disease transmission model, have been discussed and studied by many scholars, and the models allow scholars to better understand the information dissemination problems that exist in many interdisciplinary disciplines [9]–[11]. Complex network theory has been widely applied to public opinion control, rumor propagation, advertising effects, social networks, and other areas [12]–[14].Among them, many have studied the results of information dissemination in the field of disease prevention and control [15]–[17], and some have discussed information prediction models based on online social network information dissemination. More realistic multi-community overlay social networks have been proposed in recent research work and algorithm designs have been constructed to study the control of epidemic transmission by controlling overlay nodes with good results [20]. We focus on the influence of competitive awareness on the communication process in multi-information networks, and how to take effective control measures over the dissemination of information, as well as its prediction of information dissemination trends
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