Network Mining And Propagation Dynamics Analysis

Network mining and propagation dynamics analysis

Network Learning and Propagation Dynamics Analysis

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.

Computational Social Networks

This book is the first of three volumes that illustrate the concept of social networks from a computational point of view. The book contains contributions from a international selection of world-class experts, with a specific focus on practical tools, applications, and open avenues for further research (the other two volumes review issues of Security and Privacy, and Mining and Visualization in CSNs). Topics and features: presents the latest advances in CSNs, and illustrates how organizations can gain a competitive advantage by applying these ideas in real-world scenarios; discusses the design and use of a wide range of computational tools and software for social network analysis; describes simulations of social networks, the representation and analysis of social networks, and the use of semantic networks in knowledge discovery and visualization; provides experience reports, survey articles, and intelligence techniques and theories relating to specific problems in network technology.