Dynamic Chain Analysis by Bipartite Network for Medicine Selection

Abstract

Abstract 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.