In micro-blogging contexts such as Twitter,the number of content producers can easily reach tens of thousands,and many users can participate in discussion of any given topic.While many users can introduce diversity,as...In micro-blogging contexts such as Twitter,the number of content producers can easily reach tens of thousands,and many users can participate in discussion of any given topic.While many users can introduce diversity,as not all users are equally influential,it makes it challenging to identify the true influencers,who are generally rated as being interesting and authoritative on a given topic.In this study,the influence of users is measured by performing random walks of the multi-relational data in micro-blogging:retweet,reply,reintroduce,and read.Due to the uncertainty of the reintroduce and read operations,a new method is proposed to determine the transition probabilities of uncertain relational networks.Moreover,we propose a method for performing the combined random walks for the multi-relational influence network,considering both the transition probabilities for intra-and inter-networking.Experiments were conducted on a real Twitter dataset containing about 260 000 users and 2.7million tweets,and the results show that our method is more effective than TwitterRank and other methods used to discover influencers.展开更多
This study employs the random finite element method (RFEM) to analyze the wall deflection caused by excavation. The RFEM combined random fields of material properties with the FEM through the Monte Carlo simulation. A...This study employs the random finite element method (RFEM) to analyze the wall deflection caused by excavation. The RFEM combined random fields of material properties with the FEM through the Monte Carlo simulation. A well-documented excavation case history is employed to evaluate the influence of uncertainty of analysis parameters. This study shows that RFEM can provide reasonable estimations of the exceedance probability of wall deflection caused by excavation, and has the potential to be a useful tool to account for the uncertainties of material and model parameters in the numerical analysis.展开更多
基金supported by National Natural Science Foundation of China under Grants No. 60933005, No. 91124002under Grants No. 012505, No. 2011AA010702, No. 2012AA01A401, No. 2012AA01A402 (863 program)+1 种基金under Grant No.2011A010 (242)NSTM under Grants No.2012BAH38B04, No.2012BAH38B06
文摘In micro-blogging contexts such as Twitter,the number of content producers can easily reach tens of thousands,and many users can participate in discussion of any given topic.While many users can introduce diversity,as not all users are equally influential,it makes it challenging to identify the true influencers,who are generally rated as being interesting and authoritative on a given topic.In this study,the influence of users is measured by performing random walks of the multi-relational data in micro-blogging:retweet,reply,reintroduce,and read.Due to the uncertainty of the reintroduce and read operations,a new method is proposed to determine the transition probabilities of uncertain relational networks.Moreover,we propose a method for performing the combined random walks for the multi-relational influence network,considering both the transition probabilities for intra-and inter-networking.Experiments were conducted on a real Twitter dataset containing about 260 000 users and 2.7million tweets,and the results show that our method is more effective than TwitterRank and other methods used to discover influencers.
基金Project (No. NSC 99-2221-E-146-004) supported by the National Science Council
文摘This study employs the random finite element method (RFEM) to analyze the wall deflection caused by excavation. The RFEM combined random fields of material properties with the FEM through the Monte Carlo simulation. A well-documented excavation case history is employed to evaluate the influence of uncertainty of analysis parameters. This study shows that RFEM can provide reasonable estimations of the exceedance probability of wall deflection caused by excavation, and has the potential to be a useful tool to account for the uncertainties of material and model parameters in the numerical analysis.
