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| Potential Disruptive Technology Identification Method Based on Graph Representation Learning |
| Dou Yongxiang, Kai Qing, Wang Jiamin |
| School of Economics and Management, Xidian University, Xi'an 710126 |
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Abstract In addition to disrupting existing technology systems and value networks, disruptive technologies can also drive technological innovation. Most traditional disruptive technology identification methods based on bibliometrics use data from theses and patents to first construct keyword networks and sets and then artificially construct high-level data features for analysis; obtaining all the structural information is difficult, which leads to a decrease in the identification accuracy. This study introduces a semi-supervised disruptive technology identification method based on graph representation learning. First, using data from scientific and technical literature databases, a keyword-weighted network is constructed with the keyword co-occurrence frequency and journal influence. Second, the keyword network vector is obtained by learning anonymous wandering sequences using a backpropagation algorithm. Third, potentially disruptive technologies are identified by comparing the similarity between vector sequences, which can describe similarities in technology evolution, the technology to be identified, and the recognized disruptive technology. Finally, ten technologies were selected as experimental targets from recent strategic planning and prediction reports related to disruptive technologies at home and abroad. Of these technologies, this study could identify three potentially disruptive technologies and determine two pseudo-disruptive technologies to be non-disruptive technologies with pre-given five disruptive technologies.
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Received: 07 April 2022
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