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Report by Professor Zhou Bin ​ Report Title: Scale-free attributes and information dissemination of complex networks based on statistical analysis of data
Date:2021-11-29   View:



Report Title: Scale-free attributes and information dissemination of complex networks based on statistical analysis of data


Report Time: December 02, 2021 09:00--11:00


Place: 301 Conference Room, Practice Building


Speaker: Prof. Bin Zhou


Report Abstract



Report content I.


We established an innovative statistical method to analyze information dissemination data and social network big data among users of three globally well-known social platforms, Sina Weibo, Twitter and LiveJournal, and found that the probability of information recipients retweeting information disseminators has a non-linear double power-law relationship with the number of users followed by information recipients and the number of users followed by information disseminators. We merge this finding with the real user response time law of forwarded messages into a cascade model to construct a realistic and concise information dissemination model. After correcting for observational bias, the model reproduces the key topology of real-world information dissemination in separate simulations of real social networks, providing a practical approach for designing more realistic generative models of information dissemination and establishing a new paradigm research method for revealing the complexity of human behavior analysis modeling and simulation. The results were published in Nature Human Behaviour, a subjournal of Nature.


Report II.


 Scale-free properties are one of the most important topological features of complex networks in the real world, and degree distribution is a quantitative indicator of scale-free properties of complex networks, but the degree distribution itself and its quantitative statistical methods have been questioned and debated by many scholars. We propose the concept of degree distance to describe the structure of complex networks from the perspective of complex network edges, to understand the scale-free properties of complex networks again, and find that the degree distance index can better describe and understand the scale-free properties of complex networks than the current traditional degree distribution index through a large number of empirical data statistical analysis, and establish a model of the evolution mechanism of complex systems, and its model analysis and simulation results also provide a better reproduction and understanding of the empirical statistical The model analysis and simulation results also reproduce and explain the results of empirical statistics better. The model analysis and simulation results also reproduce and explain the empirical statistical results better. The analysis of the degree distribution and degree distance distribution of the model reveals that there is a concise functional relationship between the power exponents of the degree distribution and the degree distribution with a difference of one, and this functional relationship is confirmed by a large number of empirical data of complex networks.


Biography of the rapporteur.


D. from University of Science and Technology of China. He is mainly engaged in the research of complex systems, network science, and computational social science based on statistical analysis of big data, and his research results have been published in Nature subjournal Nature Human Behaviour, Proceedings of the National Academy of Sciences PNAS, and top journals in the field of sociology. His research results have been published in Nature subjournal, Nature Human Behaviour, Proceedings of the National Academy of Sciences, PNAS, and the top journal of sociology, Social Networks, etc. He has been awarded the first prize of the National Conference on Complex Networks Big Data Competition, the second prize of the Jiangsu Provincial Science and Technology Research Achievement Award, and the outstanding backbone teacher of Jiangsu Provincial Youth and Blue Project.


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