With the fast-growth of mobile social network, people＇ s interactions are frequently marked with location information, such as longitude and latitude of visited base station. This boom of data has led to considerable...With the fast-growth of mobile social network, people＇ s interactions are frequently marked with location information, such as longitude and latitude of visited base station. This boom of data has led to considerable interest in research fields such as user behavior mining, trajectory discovery and social demographics. However, there is little research on community discovery in mobile social networks, and this is the problem this work tackles with. In this work, we take advantage of one simple property that people in different locations often belong to different social circles in order to discover communities in these networks. Based on this property, which we referred to as Location-lnteraction Disparity （LID）, we proposed a state network and then define a quality function evaluating community detection results. We also propose a hybrid community- detection algorithm using LID tor discovering location-based communities effectively and efficiently. Experiments on synthesis networks show that this algorithm can run effectively in time and discover communities with high precision. In realworld networks, the method reveals people＇s different social circles in different places with high efficiency.展开更多
In this paper, we propose a nearly analytic exponential time difference （NETD） method for solving the 2D acoustic and elastic wave equations. In this method, we use the nearly analytic discrete operator to approxima...In this paper, we propose a nearly analytic exponential time difference （NETD） method for solving the 2D acoustic and elastic wave equations. In this method, we use the nearly analytic discrete operator to approximate the high-order spatial differential operators and transform the seismic wave equations into semi-discrete ordinary differential equations （ODEs）. Then, the converted ODE system is solved by the exponential time difference （ETD） method. We investigate the properties of NETD in detail, including the stability condition for 1-D and 2-D cases, the theoretical and relative errors, the numerical dispersion relation for the 2-D acoustic case, and the computational efficiency. In order to further validate the method, we apply it to simulating acoustic/elastic wave propagation in mul- tilayer models which have strong contrasts and complex heterogeneous media, e.g., the SEG model and the Mar- mousi model. From our theoretical analyses and numerical results, the NETD can suppress numerical dispersion effectively by using the displacement and gradient to approximate the high-order spatial derivatives. In addition, because NETD is based on the structure of the Lie group method which preserves the quantitative properties of differential equations, it can achieve more accurate results than the classical methods.展开更多
基金supported by the National High Technology Research and Development Program of China under Grant No.2014AA015103Beijing Natural Science Foundation under Grant No.4152023+1 种基金the National Natural Science Foundation of China under Grant No.61473006the National Science and Technology Support Plan under Grant No.2014BAG01B02
文摘With the fast-growth of mobile social network, people＇ s interactions are frequently marked with location information, such as longitude and latitude of visited base station. This boom of data has led to considerable interest in research fields such as user behavior mining, trajectory discovery and social demographics. However, there is little research on community discovery in mobile social networks, and this is the problem this work tackles with. In this work, we take advantage of one simple property that people in different locations often belong to different social circles in order to discover communities in these networks. Based on this property, which we referred to as Location-lnteraction Disparity （LID）, we proposed a state network and then define a quality function evaluating community detection results. We also propose a hybrid community- detection algorithm using LID tor discovering location-based communities effectively and efficiently. Experiments on synthesis networks show that this algorithm can run effectively in time and discover communities with high precision. In realworld networks, the method reveals people＇s different social circles in different places with high efficiency.
文摘In this paper, we propose a nearly analytic exponential time difference （NETD） method for solving the 2D acoustic and elastic wave equations. In this method, we use the nearly analytic discrete operator to approximate the high-order spatial differential operators and transform the seismic wave equations into semi-discrete ordinary differential equations （ODEs）. Then, the converted ODE system is solved by the exponential time difference （ETD） method. We investigate the properties of NETD in detail, including the stability condition for 1-D and 2-D cases, the theoretical and relative errors, the numerical dispersion relation for the 2-D acoustic case, and the computational efficiency. In order to further validate the method, we apply it to simulating acoustic/elastic wave propagation in mul- tilayer models which have strong contrasts and complex heterogeneous media, e.g., the SEG model and the Mar- mousi model. From our theoretical analyses and numerical results, the NETD can suppress numerical dispersion effectively by using the displacement and gradient to approximate the high-order spatial derivatives. In addition, because NETD is based on the structure of the Lie group method which preserves the quantitative properties of differential equations, it can achieve more accurate results than the classical methods.
