A large number of community discovery algorithms have been proposed in the last decade. Recently, the sharp increase of network scale has become a great challenge for traditional community discovery algorithms. Label ...A large number of community discovery algorithms have been proposed in the last decade. Recently, the sharp increase of network scale has become a great challenge for traditional community discovery algorithms. Label propagation algorithm is a semi-supervised machine learning method, which has linear time complexity when coping with large scale networks. However, the output result has less stability and the quality of the output communities still remains to be improved. Therefore, we propose a novel coreleader based label propagation algorithm for community detection called CLBLPA. Firstly, we find core leaders of potential community by using a greedy method. Then we utilize the label influence potential to guide the process of label propagation. Thus we can accelerate the convergence of algorithm and improve the stability of the output. Experimental results on synthetic datasets and real networks show that CLBLPA can significantly improve the quality of the output communities.展开更多
In this paper I apply the Quantile Regression model that suits for the different contribution of the attributes surrounding different levels of film revenues.The regression coefficients from this model reflects the co...In this paper I apply the Quantile Regression model that suits for the different contribution of the attributes surrounding different levels of film revenues.The regression coefficients from this model reflects the correlation between the film revenue and the various attributes(production budget,popularity,runtime,vote average and vote count).The empirical analysis result shows that QR coefficients vary across different intervals of film revenue.This implies that the size of the effect for the influencing factors differ between profitability quantiles of films.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 61272277, 41301409, 41571390the Fundamental Research Funds for the Central Universities under Grant No. 274742
文摘A large number of community discovery algorithms have been proposed in the last decade. Recently, the sharp increase of network scale has become a great challenge for traditional community discovery algorithms. Label propagation algorithm is a semi-supervised machine learning method, which has linear time complexity when coping with large scale networks. However, the output result has less stability and the quality of the output communities still remains to be improved. Therefore, we propose a novel coreleader based label propagation algorithm for community detection called CLBLPA. Firstly, we find core leaders of potential community by using a greedy method. Then we utilize the label influence potential to guide the process of label propagation. Thus we can accelerate the convergence of algorithm and improve the stability of the output. Experimental results on synthetic datasets and real networks show that CLBLPA can significantly improve the quality of the output communities.
文摘In this paper I apply the Quantile Regression model that suits for the different contribution of the attributes surrounding different levels of film revenues.The regression coefficients from this model reflects the correlation between the film revenue and the various attributes(production budget,popularity,runtime,vote average and vote count).The empirical analysis result shows that QR coefficients vary across different intervals of film revenue.This implies that the size of the effect for the influencing factors differ between profitability quantiles of films.
