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| Identification of Technical R&D Cooperation Partners Based on Heterogeneous Graph Embedding and Link Prediction |
| Pan Hong1, Wang Ming2, Zhao Kai2, Zhai Liang2, Liang Guoqiang2, Zhai Dongsheng2 |
1.School of Accountancy, Beijing Wuzi University, Beijing 101149
2.College of Economics and Management, Beijing University of Technology, Beijing 100124 |
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Abstract Technology research and development (R&D) partner identification is key for increasing innovation performance. To address the issues of limited attribute portrayal of technology R&D subjects and limited recognition accuracy owing to the sparse cooperation matrix in existing methods, this study proposes a technology R&D partner identification model (technology development partner identification integrating heterogeneous graph embedding link prediction, HGE-LP-TDPI) that integrates heterogeneous graph embedding and link prediction. First, the ontology structure of the technology R&D partner identification domain was constructed, and semantic extraction of patent specifications was performed based on large language models (LLMs) to generate a heterogeneous graph containing multidimensional technology associations of technology R&D subjects. Second, meta-paths characterizing technology associations were designed, and semantic information was aggregated using a long short-term memory (LSTM) temporal encoder with a multilevel attention mechanism. Finally, we identified partners based on a link prediction algorithm. Empirical studies in the field of electrochemical energy storage reveal the following: first, the HGE-LP-TDPI model significantly outperformed the benchmark model in terms of key indicators such as the area under curve (AUC) value (it reaches 95.62%), confirming the effectiveness of multidimensional technology linkage fusion and heterogeneous graph embedding in solving the problem of data sparsity; second, meta-path weighting analysis reveals that the technical problem drive was the core factor of cooperation formation, and the application domain and technical efficacy had the second highest influence; third, attribute ablation experiments show that technical dimension and knowledge dimension attributes had the highest contribution to the model results.
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Received: 12 February 2025
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