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Identifying firm-specific technology opportunities: Heterogeneous graph neural network-based link prediction

Author

Listed:
  • Wu, Yingwen
  • Lin, Zhouzhou
  • Ji, Yangjian
  • Gu, Fu

Abstract

A firm’s technological innovation is influenced by both its internal capabilities and external technological trends. However, previous firm-specific technology opportunity discovery (TOD) studies have predominantly focused on structural associations between technologies within a firm’s internal and external contexts, with limited exploration of deeper semantic relationships. This paper proposes a novel firm-specific TOD approach that considers both structural and semantic associations. Our methodology consists of four modules: (1) collecting patent data; (2) constructing a technological innovation heterogeneous graph; (3) identifying the target firm’s technology opportunities using Multi-Attention Graph Link Prediction (MAG-LP), which captures both structural and semantic information from the graph; and (4) evaluating technology opportunities using indicators of technology competitiveness, technology growth, and technology maturity. The efficiency and effectiveness of our proposed approach are demonstrated through its application to Honda Motor Company. This work contributes to a comprehensive understanding of potential R&D directions for the target firm.

Suggested Citation

  • Wu, Yingwen & Lin, Zhouzhou & Ji, Yangjian & Gu, Fu, 2026. "Identifying firm-specific technology opportunities: Heterogeneous graph neural network-based link prediction," Technovation, Elsevier, vol. 151(C).
    • Handle: RePEc:eee:techno:v:151:y:2026:i:c:s0166497225002378
    DOI: 10.1016/j.technovation.2025.103405
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