동적 확률 재규격화를 이용한 네트워크 연쇄 관계 해석
Analysis of Network Chain using Dynamic Convolution Model
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초록
Many classification studies for the community of densely-connected nodes are limited to the comprehensive analysis for detectingthe communities in probabilistic networks with nodes and edge of the probabilistic distribution because of the difficulties of theprobabilistic operation. This study aims to use convolution method for operating nodes and edge of probabilistic distribution. For theprobabilistic hierarchy network with nodes and edges of the probabilistic distribution, the model of this study detects the communitiesof nodes to make the new probabilistic distribution with two distribution. The results of our model was verified through comparingwith Monte-carlo Simulation and other community-detecting methods.
Abstract
Many classification studies for the community of densely-connected nodes are limited to the comprehensive analysis for detectingthe communities in probabilistic networks with nodes and edge of the probabilistic distribution because of the difficulties of theprobabilistic operation. This study aims to use convolution method for operating nodes and edge of probabilistic distribution. For theprobabilistic hierarchy network with nodes and edges of the probabilistic distribution, the model of this study detects the communitiesof nodes to make the new probabilistic distribution with two distribution. The results of our model was verified through comparingwith Monte-carlo Simulation and other community-detecting methods.
참고문헌 (21)
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