Predicting Transactions Among Agents in Patent Transfer Weighted Networks for New Energy
Wu Yuying(), Sun Ping, He Xijun, Jiang Guorui
College of Economics and Management, Beijing University of Technology, Beijing 100124, China Research Base of Beijing Modern Manufacturing Development, Beijing 100124, China
[Objective] This paper examines the structure of weighted network for patent transfers as well as the characteristics of agents, aiming to predict transaction opportunities and promote the connection of technology supply and demand. [Methods] First, we constructed a weighted network for patented technology transactions based on data from 2012 to 2016. Then, we used the entropy method to combine its structure and contents. Finally, we used the BP neural network to predict transaction opportunities and weights. [Results] The prediction accuracy by the proposed method, which combined the structure index RA and the content index Cosine, was the highest. The prediction error was also reduced by using the real and structure weights of the network to predict the link weight. [Limitations] More research is needed to study the Node properties and network evolution mechanism. [Conclusions] The link prediction method has a higher precision, which help us find potential supply and demand agents of the technology patent transfers.
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