Early Identification of Significant Patents Using Heterogeneous Applicant-Citation Networks Based on the Chinese Green Patent Data: Fid-Bau Portal

Early Identification of Significant Patents Using Heterogeneous Applicant-Citation Networks Based on the Chinese Green Patent Data

Xipeng Liu / Xinmiao Li

Abstract

With the deterioration of the environment and the acceleration of resource consumption, green patent innovation focusing on environmental protection fields has become a research hot-spot around the world. Previous researchers constructed homogeneous information networks to analyze the influence of patents based on citation ranking algorithms. However, a patent information network is a complex network containing multiple pieces of information (e.g., citation, applicant, inventor), and the use of a single information network will result in incomplete information or information loss, and the obtained results are biased. In addition, scholars constructed centrality indicators to assess the importance of patents with less consideration of the age bias problem of algorithms and models, and the results obtained are inaccurate. In this paper, based on the Chinese green patent (CNGP) dataset from 1985 to 2020, a CNGP heterogeneous applicant-citation network is constructed, and the rescaling method and normalization procedure are used to solve the age bias. The results illustrate that the method proposed in this paper is able to identify significant patents earlier, and the performance of the rescaled indegree (R_ID) works best such as the IR score is 17.32% in the top 5% of the rankings, and it is the best in the constructed dynamic heterogeneous networks as well. In addition, the constructed heterogeneous information network has better results compared with the traditional homogeneous information network, such as the NIR score of R_ID metrics can be improved by 2% under the same condition. Therefore, the analysis method proposed in this paper can reasonably evaluate the quality of patents and identify significant patents earlier, thus providing a new method for scientists to measure the quality of patents.