基于图神经网络的技术识别链接预测方法研究

Technology Recognition and Link Prediction Method Based on GNN

【目的】通过融合了时间特征的专利IPC共现网络,训练图神经网络模型实现链接预测方法,为技术发现和知识供给提供参考。【方法】采集“隐私保护”专利数据构建专利IPC共现网络,构建节点的时间分布、时间稳定性和时间关注度特征,训练GraphSAGE模型,得到IPC节点表示及其之间的链接预测得分,为技术机会挖掘提供辅助和支持。【结果】基于图神经网络模型的链接预测方法相对于基于节点相似性的传统链接预测方法以及图游走算法Node2Vec在AUC指标上提升约30%。【局限】图神经网络作为深度学习模型在训练耗时上存在一定劣势。【结论】基于图神经网络的链接预测方法具有较高的预测精度,结合时间特征后能够捕捉节点的动态特征,为技术发现等任务提供有价值的参考。

[Objective]This paper integrates time features into a patent IPC co-occurrence network and trains the GNN model for link prediction.It aims to provide a reference for technology discovery and knowledge supply.[Methods]First,we collected the patent data on“privacy protection”to construct an IPC co-occurrence network.Then,we assigned time distribution,stability,and attention features to the network nodes.Third,we trained the GraphSAGE model to obtain the IPC nodes’representation and predict the link score between them.It provides assistance and support for technology opportunity mining.[Results]Compared with the traditional link prediction method based on node similarity and the Node2Vec,the proposed model achieved a 30%improvement in the AUC metric.[Limitations]As a deep learning model,GNN has some disadvantages in training time.[Conclusions]Our new link prediction method exhibits high prediction accuracy.Combined with the time characteristics,it can capture the dynamic characteristics of nodes and provide valuable insights for technology discovery and other tasks.