Key generic technology prediction in patent citation using graph neural networks

With the rapid advancement of the Fourth Industrial Revolution, international competition in technology and industry is intensifying. However, in the era of big data and large-scale science, making accurate judgments about the key areas of technology and innovative trends has become exceptionally difficult. This paper constructs a patent indicator evaluation system based on the dimensions of key and generic patent citation, integrates graph neural network modeling to predict key common technologies, and confirms the effectiveness of the method using the field of genetic engineering as an example. According to the LDA topic model, the main technical R&D directions in genetic engineering are genetic analysis and detection technologies, the application of microorganisms in industrial production, virology research involving vaccine development and immune responses, high-throughput sequencing and analysis technologies in genomics, targeted drug design and molecular therapeutic strategies..., These datasets were obtained by the Incopat patent database for cited patents (2013-2022) in the field of genetic engineering.
Details for the datasets are provided in the README file.
This directory contains the selection of the patent datasets.
1) Table of key generic indicators for nodes (partial 1).csv
This file consists of 10 indicators of patents: technical coverage, patent families, patent family citation, patent cooperation, enterprise-enterprise cooperation, industry-university-research cooperation, claims, citation frequency, layout countries, and layout countries.
2) Table of key generic indicators for nodes (partial 2).csv
This file consists of 10 indicators of patents: technical convergence, cited countries, inventors, citations, homologous countries/areas, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and PageRank.
3) patent.content
The content file contains descriptions of the patents in the following format: <ID_number> &l..., , # Key generic technology prediction in patent citation using graph neural networks

This README file was generated on 2023-11-25 by Mingli Ding.

## GENERAL INFORMATION

1. Author Information
Investigators Contact Information
Name: Mingli Ding; Wangke Yu; Shuhua Wang
Institution: Jingdezhen Ceramic University
Address: Jingdezhen, Jiangxi, China
Email: [[e-mail]](mailto:[e-mail])
2. Date of data collection:2013-2022

## DATA & FILE OVERVIEW

1. File List:

A) Table of key generic indicators for nodes (partial 1).csv

B) Table of key generic indicators for nodes (partial 2).csv

C) patent.content

D) patent.cites

E) Graph neural network modeling highest accuracy for different dimensions.csv

F) Prediction effects of key generic technologies.csv

### DATA-SPECIFIC INFORMATION FOR: Table of key generic indicators for nodes (partial 1).csv

1. Number of variables: 10
2. Number of cases/rows: 72489
3. Variable List:

* technical coverage: number ...