Many phenomena in the fields of computer science, biology, sociology, and economics can be described as transmission dynamics on complex networks, and transmission dynamics mainly includes information transmission, disease transmission, and computer virus transmission. In many real propagation phenomena, we often want to know their propagation mechanism and law on complex networks, as well as their prediction methods and control means. Clarifying the above problems allows people to have a clear and comprehensive understanding of the evolution mechanism, propagation process, and steady-state of real phenomena. At the same time, it also provides some necessary theoretical support for predicting and controlling real systems. In recent years, many concepts and methods of statistical physics have also been successfully used in the modeling and calculation of complex networks, such as statistical mechanics, self-organization theory, critical and phase transition theory, seepage theory, and so on. In complex networks, seepage can simulate and describe the growth and evolution characteristics of many natural and social systems. Based on the classical network seepage, people have carried out a lot of research on the explosive seepage in the process of network growth in recent years. Using the seepage method, researchers have drawn many conclusions and brought new ideas in the research directions of network propagation and cascade failure. Based on these theories, many researchers have quantitatively analyzed the influence of many factors on transmission path and transmission mechanism, and then discussed the effect of the control strategy.
With the rapid development of network science, the study of link prediction is closely related to the structure and evolution of networks. Meanwhile, such research can also help us understand the evolution mechanism of complex networks theoretically, so as to better help us deduce the propagation dynamics of complex networks. At present, the application of large-scale real data to complex networks still lacks in-depth analysis and research. The explosive growth of data in the network has brought new challenges to the prediction of the nodes relationship and propagation process. In order to precisely predict future data, it is necessary to design efficient and accurate models, and one of the solutions is to optimize network nodes, which can improve the efficiency of data transmission. Furthermore, how to predict the possibility of links on nodes and how to truly simulate the propagation mechanism between nodes are also urgent problems to be dealt with.
The goal of this Research Topic in Frontiers in Physics is to welcome the contribution of complex networks, a rapidly developing research field. We encourage articles that use multidisciplinary methods for complex network data mining, such as machine learning, information theory, applied mathematics, and computational statistical physics. Potential topics include but are not limited to the following:
• Trend analysis of social network information dissemination
• Analysis on the spread trend of infectious diseases
• Analysis of computer virus transmission process
• Link prediction on social networks
• Behavior analysis on social networks
• Network state prediction
• Pattern recognition of behaviors
• Personalized recommender systems
Many phenomena in the fields of computer science, biology, sociology, and economics can be described as transmission dynamics on complex networks, and transmission dynamics mainly includes information transmission, disease transmission, and computer virus transmission. In many real propagation phenomena, we often want to know their propagation mechanism and law on complex networks, as well as their prediction methods and control means. Clarifying the above problems allows people to have a clear and comprehensive understanding of the evolution mechanism, propagation process, and steady-state of real phenomena. At the same time, it also provides some necessary theoretical support for predicting and controlling real systems. In recent years, many concepts and methods of statistical physics have also been successfully used in the modeling and calculation of complex networks, such as statistical mechanics, self-organization theory, critical and phase transition theory, seepage theory, and so on. In complex networks, seepage can simulate and describe the growth and evolution characteristics of many natural and social systems. Based on the classical network seepage, people have carried out a lot of research on the explosive seepage in the process of network growth in recent years. Using the seepage method, researchers have drawn many conclusions and brought new ideas in the research directions of network propagation and cascade failure. Based on these theories, many researchers have quantitatively analyzed the influence of many factors on transmission path and transmission mechanism, and then discussed the effect of the control strategy.
With the rapid development of network science, the study of link prediction is closely related to the structure and evolution of networks. Meanwhile, such research can also help us understand the evolution mechanism of complex networks theoretically, so as to better help us deduce the propagation dynamics of complex networks. At present, the application of large-scale real data to complex networks still lacks in-depth analysis and research. The explosive growth of data in the network has brought new challenges to the prediction of the nodes relationship and propagation process. In order to precisely predict future data, it is necessary to design efficient and accurate models, and one of the solutions is to optimize network nodes, which can improve the efficiency of data transmission. Furthermore, how to predict the possibility of links on nodes and how to truly simulate the propagation mechanism between nodes are also urgent problems to be dealt with.
The goal of this Research Topic in Frontiers in Physics is to welcome the contribution of complex networks, a rapidly developing research field. We encourage articles that use multidisciplinary methods for complex network data mining, such as machine learning, information theory, applied mathematics, and computational statistical physics. Potential topics include but are not limited to the following:
• Trend analysis of social network information dissemination
• Analysis on the spread trend of infectious diseases
• Analysis of computer virus transmission process
• Link prediction on social networks
• Behavior analysis on social networks
• Network state prediction
• Pattern recognition of behaviors
• Personalized recommender systems