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| Patent Transaction Prediction for Technology Transfer: Perspective Based on Graph Neural Networks and Corporate Portraits |
| Qian Minghui1,2, Zhao Mengchun1, Wang Chi1, Wang Mingyu1 |
1.School of Information Resource Management, Renmin University of China, Beijing 100872
2.Yangtze River Economic Zone Research Institution of RUC, Beijing 100872 |
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Abstract The acceleration of the transformation of scientific and technological achievements is a crucial initiative for implementing the innovation-driven development strategy and advancing high-quality economic and social development. In this study, a heterogeneous graph neural network model based on enterprise and patent profiles, Company-Patent Profile Relational Graph Convolutional Networks (CP-RGCN), is proposed to predict potential patent-transaction partners. The model is validated using patent data from the artificial intelligence field. Experimental results demonstrate that CP-RGCN outperforms baseline models (Support Vector Machine, Random Forest, Graph Convolutional Network, Relational Graph Convolutional Network, Graph Sample and Aggregate, and Graph Attention Network) in key metrics such as Mean Reciprocal Rank and Hits@K. A feature importance analysis reveals that integrating enterprise and patent profile features within a heterogeneous graph neural network framework significantly enhances prediction accuracy while improving model interpretability and reliability. The proposed CP-RGCN model improves the identification of potential technology transfer opportunities and the commercialization efficiency of scientific and technological achievements. Furthermore, this study provides valuable insights and references for the application of heterogeneous graph neural networks to broader domains and scenarios.
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Received: 21 April 2025
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