Identifying and evaluating R&D partners via patent-based multilayer networks from the perspective of knowledge complementarity: A case study of unmanned ship technology

Abstract

With the rapid pace of technological development, innovation is crucially related to cross-disciplinary collaboration and resource sharing. To address this challenge, researchers need to break existing knowledge boundaries by seeking potential collaborations. However, when identifying and evaluating partners, existing studies have failed to explore patent technology information, patent citation information, and the complex collaborative relationships among patent holders. This results in an incomplete and inaccurate identification and evaluation of potential R&D partners. In response, this study proposes a multilayer network framework based on patent data to identify and evaluate potential R&D partners. This framework considers patent technical information, citation information, and collaboration among patentees. The method consists of three key stages. First, a network of collaboration between patentees, a knowledge network, and a patent citation network are constructed. Second, complementarity metrics are proposed on the basis of the combination of similarity and heterogeneity to identify potential R&D partners. Finally, potential R&D partners are further evaluated and determined by analyzing competitive or collaborative relationships. The proposed method is verified through a case study of unmanned ship technology. This method provides a more comprehensive perspective for R&D partner identification and evaluation. Moreover, the results provide a reference for enterprises to improve their collaboration efficiency.