[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.
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