Open AccessDynamic Chain Analysis by Bipartite Network for Medicine Selection
Author(s) -
Xin Yin,
Yige Guo,
Zhiwei Cao,
Min Xiong
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1621/1/012061
Subject(s) - bipartite graph , chain (unit) , computer science , selection (genetic algorithm) , order (exchange) , data mining , operations research , mathematical optimization , risk analysis (engineering) , artificial intelligence , theoretical computer science , mathematics , business , graph , physics , finance , astronomy
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.