文献类型：期刊文献

英文题名：Dynamic Chain Analysis by Bipartite Network for Medicine Selection

作者：Yin, Xinming[1,2]; Guo, Yi[1]; Cao, Zhiwei[2]; Xiong, Min[2]

机构：[1] School of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Third Research Institute, Ministry of Public Security, Shanghai, 200031, China

年份：2020

卷号：1621

期号：1

外文期刊名：Journal of Physics: Conference Series

收录：EI(收录号：20204009255735)

语种：英文

摘要：The rapid development of society has brought about the uncertainty of social relations, and the structure of social networks is constantly changing. Chain forecast, as an effective method, plays an increasingly important role in walks of life in understanding the dynamic nature of the network and determining future relationships, combining the structural characteristics of the present status of the network to foresee the possible existence of future network nodes, this paper proposes a Chain forecast method for Disease treatment and a Chain forecast method based on bipartite networks (such as treatment correspondent diagrams). In order to verify the forecast effect of the method, we selected several Chain forecast algorithms for check. The results prove that our proposed method is better than other methods based on Chain forecast. ? 2020 Published under licence by IOP Publishing Ltd.