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&amp;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: &lt;ID_number&gt; &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 &amp; 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 ...随着第四次工业革命的快速推进，全球科技与产业领域的国际竞争日趋激烈。然而，在大数据与大科学时代，精准研判技术关键领域与创新趋势已变得异常艰难。本文构建了基于核心与通用专利引文维度的专利指标评价体系，融合图神经网络（Graph Neural Network）建模以预测关键通用技术，并以基因工程领域为例验证了该方法的有效性。基于潜在狄利克雷分配（LDA）主题模型的分析结果显示，基因工程领域的主要技术研发方向包括：遗传分析与检测技术、微生物在工业生产中的应用、涉及疫苗开发与免疫应答的病毒学研究、基因组学中的高通量测序与分析技术、靶向药物设计与分子治疗策略……本数据集源自Incopat专利数据库中基因工程领域2013-2022年的被引专利。数据集的详细信息请参见README文件。本目录包含精选的专利数据集： 1) 节点关键通用指标表（第一部分）.csv：该文件包含10项专利相关指标：技术覆盖范围、专利族、专利族引文、专利合作、企业间合作、产学研合作、权利要求、被引频次、布局国家、布局国家。 2) 节点关键通用指标表（第二部分）.csv：该文件包含10项专利相关指标：技术融合度、被引国家、发明人、被引次数、同源国家/地区、度中心性、接近中心性、介数中心性、特征向量中心性以及PageRank。 3) patent.content：该内容文件包含专利描述，格式为：&lt;ID_number&gt; &l..., , # 基于图神经网络的专利引文关键通用技术预测。 本README文件由丁明利于2023年11月25日生成。 ## 基本信息 1. 作者信息 研究者联系方式 姓名：丁明利、王可宇、王舒华 所属机构：景德镇陶瓷大学 地址：中国江西省景德镇市 邮箱：<[e-mail]> 2. 数据采集日期：2013-2022年 ## 数据与文件概览 1. 文件列表： A) 节点关键通用指标表（第一部分）.csv B) 节点关键通用指标表（第二部分）.csv C) patent.content D) patent.cites E) 不同维度下图神经网络建模最高准确率.csv F) 关键通用技术预测效果.csv ### 节点关键通用指标表（第一部分）.csv 的特定数据信息 1. 变量数量：10个 2. 样本/行数：72489条 3. 变量列表： * technical coverage：数量……