The maximization of personalized influence is a branch of maximizing the influence of social networks, and the goal is to target specific social network users and mine the set of initial impact diffusion users that ha...The maximization of personalized influence is a branch of maximizing the influence of social networks, and the goal is to target specific social network users and mine the set of initial impact diffusion users that have made the most impact. However, most of the existing methods are based on the IC model and the LT model. The prediction of the impact of these two models on the nodes depends on the Monte Carlo simulation. In order to avoid Monte Carlo simulation time and simulate real life more, this paper introduces the heat diffusion model into the problem of maximizing the influence of personalization. The heat diffusion process was used to simulate the diffusion process of information influence. And the thermal energy was applied to measure the impact on the target users, and cluster candidate users. The cluster center as a seed node was proposed to spread information and maximizing the impact on specific users. The comparison experiments on real social networks show that the personalized maximization algorithm based on the thermal diffusion model has better time performance and diffusion effect than the traditional diffusion model.展开更多
Much of our current knowledge of biology has been constructed based on population-average measurements. However, advances in single-cell analysis have demonstrated the omnipresent nature of cell-to-cell variability in...Much of our current knowledge of biology has been constructed based on population-average measurements. However, advances in single-cell analysis have demonstrated the omnipresent nature of cell-to-cell variability in any population. On one hand, tremendous efforts have been made to examine how such variability arises, how it is regulated by cellular networks, and how it can affect cell-fate decisions by single cells. On the other hand, recent studies suggest that the variability may carry valuable information that can facilitate the elucidation of underlying regulatory networks or the classification of cell states. To this end, a major challenge is determining what aspects of variability bear significant biological meaning. Addressing this challenge requires the development of new computational tools, in conjunction with appropriately chosen experimental platforms, to more effectively describe and interpret data on cell- cell variability. Here, we discuss examples of when population heterogeneity plays critical roles in determining biologically and clinically significant phenotypes, how it serves as a rich information source of regulatory mechanisms, and how we can extract such information to gain a deeper understanding of biological systems.展开更多
文摘The maximization of personalized influence is a branch of maximizing the influence of social networks, and the goal is to target specific social network users and mine the set of initial impact diffusion users that have made the most impact. However, most of the existing methods are based on the IC model and the LT model. The prediction of the impact of these two models on the nodes depends on the Monte Carlo simulation. In order to avoid Monte Carlo simulation time and simulate real life more, this paper introduces the heat diffusion model into the problem of maximizing the influence of personalization. The heat diffusion process was used to simulate the diffusion process of information influence. And the thermal energy was applied to measure the impact on the target users, and cluster candidate users. The cluster center as a seed node was proposed to spread information and maximizing the impact on specific users. The comparison experiments on real social networks show that the personalized maximization algorithm based on the thermal diffusion model has better time performance and diffusion effect than the traditional diffusion model.
文摘Much of our current knowledge of biology has been constructed based on population-average measurements. However, advances in single-cell analysis have demonstrated the omnipresent nature of cell-to-cell variability in any population. On one hand, tremendous efforts have been made to examine how such variability arises, how it is regulated by cellular networks, and how it can affect cell-fate decisions by single cells. On the other hand, recent studies suggest that the variability may carry valuable information that can facilitate the elucidation of underlying regulatory networks or the classification of cell states. To this end, a major challenge is determining what aspects of variability bear significant biological meaning. Addressing this challenge requires the development of new computational tools, in conjunction with appropriately chosen experimental platforms, to more effectively describe and interpret data on cell- cell variability. Here, we discuss examples of when population heterogeneity plays critical roles in determining biologically and clinically significant phenotypes, how it serves as a rich information source of regulatory mechanisms, and how we can extract such information to gain a deeper understanding of biological systems.
