新能源领域专利转让加权网络中主体间技术交易机会预测

Predicting Transactions Among Agents in Patent Transfer Weighted Networks for New Energy

[目的]通过挖掘专利转让加权网络主体信息及结构特征对交易机会进行预测,促进技术供需有效对接。[方法]采集新能源领域2012年–2016年数据构建专利转让加权网络,通过熵权法融合网络结构与内容指标,结合网络真实权重与结构权重,利用BP神经网络预测技术交易机会及其权重。[结果]融合结构指标RA与内容指标Cosine的预测精度达到94.28%,在所有指标组合中最高;结合网络真实权重与结构权重预测链接权重,预测误差有所降低。[局限]模型未充分考虑节点属性及网络演化机制。[结论]链路预测方法具有较高预测精度,能更全面挖掘专利技术交易网络中潜在供需主体对及其权重,对实践具有一定指导意义。

[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.