Haze Pollution Structure Learning

The region structure learning of haze pollution is studied by using the complex network analysis method.The PM2.5 data of each hour from 2015 to 2018 in 363 cities of China are collected.Then the change of concentration of PM2.5 in those cities in the recent four years is analyzed.Based on the complex graphical model method,the change of hubs and structure of the haze pollution network among the 363 cities is studied.The results show that:after the haze governance,the effect of nationwide haze control has been significantly improved,but the haze control effect in Beijing and northeast China is better than that in northwest China.Haze control needs to focous on the central cities and the regions that they locate in;to carry out haze governance,we should not only consider the differences among different communities,but also cooperate with each other within the same community to achieve better haze governance results.Most networks exhibit modular structure.Community detection algorithms detect the modular structure by identifying a division of network’s nodes into groups.How to compare those different algorithms is interesting question.In chapter 3,we give a criterion by using the differential privacy.A new set of perturbation networks is constructed by varying privacy degree.Then we compare the different different community detection methods though the community detection results.Experiments have shown that privacy protection of the edges of a vectorless network protects individual privacy to some extent but loses the accuracy of the network community’s discoveries.As the degree of privacy protection increases,the accuracy of community discoveries decreases.And using modularity is a superior community detection method in those community detection algorithms.