DOI: 10.5061/dryad.nk98sf803
title: Key generic technology prediction in patent citation using graph neural networks

description: 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, genetically modified crop improvement. The
 accuracy of predicting key generic technologies related to graph neural
 networks reaches 97.67%. Based on patent citation theory and the graph
 neural network models, this paper considers the structural and technical
 attributes of cited patents, providing theoretical and empirical evidence
 for technology prediction, and possessing certain theoretical and
 practical value.
description: 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;
 &lt;technical_attributes&gt; + &lt;class_label&gt;. Each
 line contains two patent ID numbers. The first entry is the ID number of
 the patent being cited and the second publish number stands for the patent
 which contains the citation. The direction of the link is from right to
 left. If a line is represented by "patent1 patent2" then the
 link is "patent2-&gt;patent1". 4)
 patent.cites The first entry in each line contains the
 unique string ID number of the patents followed by binary values
 indicating whether the value of each patent exceeds the average of the
 corresponding indicator (indicated by 1) or absent (indicated by 0) in the
 patent. Finally, the last entry in the line contains the class label of
 the patent. 5) Graph neural network modeling highest
 accuracy for different dimensions.csv This file shows
 the best accuracies of GCN, SAGE, and GAT models in different
 dimensions. 6) Prediction effects of key generic
 technologies.csv This file shows the accuracies of
 GCN, SAGE, and GAT models in different epochs.
description: # 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:
 [mlding1@163.com](mailto:mlding1@163.com) 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 of national economic
 classifications * patent families: number of patent families * patent
 family citation: patent family average annual citation frequency * patent
 cooperation: whether there are more than two applicants who have jointly
 applied for a patent. * enterprise-enterprise cooperation: whether more
 than two enterprises have jointly applied for a patent. *
 industry-university-research cooperation: whether any enterprises have
 applied for a patent jointly with universities or research institutions. *
 claims: number of claims * citation frequency: average annual citation
 frequency * layout countries: number of layout countries * layout
 countries: age of patents ### DATA-SPECIFIC INFORMATION FOR: Table of key
 generic indicators for nodes (partial 2).csv 1. Number of variables: 10 2.
 Number of cases/rows: 72489 3. Variable List: * technical convergence:
 number of deputy IPCs (International Patent Classification) * cited
 countries: number of cited countries * inventors: number of inventors *
 citations: number of forward citing times * homologous countries/areas:
 number of homologous countries/areas * degree centrality: the degree to
 which a node in a network is associated with all other nodes can be
 calculated, and is the simplest indicator to quantify the influence of a
 node. * closeness centrality: whether a node is at the core of a
 technological network, indicating how close the node is to all other nodes
 in the network. * betweenness centrality: number of multiple other nodes
 when it is on the shortest path, characterizes the node as having a strong
 resource control ability in the network. * eigenvector centrality: it is a
 measure of the importance of nodes in a graph, which is based on the idea
 that the centrality of a node is a function of the centrality of its
 neighboring nodes. * PageRank: an algorithm used to rank the importance of
 nodes on the web, defined as a function on a collection of web pages that
 assigns a positive real number to each web page to indicate its
 importance, and these values form a vector as a whole. ### DATA-SPECIFIC
 INFORMATION FOR: patent.content 1. Number of variables: 22 2. Number of
 cases/rows: 72489 3. Variable List: * ID: ID number of patents *
 variables2-21: the same as the variables in file A) and file B) * label:
 CORE (the patent is key generic technology) or NON (the patent is not key
 generic technology). ### DATA-SPECIFIC INFORMATION FOR: patent.cites 1.
 Number of variables: 2 2. Number of cases/rows: 72489 3. Variable List: *
 source: the ID number of the cited patent * target: the ID number of
 citing patent ### DATA-SPECIFIC INFORMATION FOR: Graph neural network
 modeling highest accuracy for different dimensions.csv 1. Number of
 variables: 4 2. Number of cases/rows: 3 3. Variable List: * dimensions of
 graph network: 4 * dimensions of graph network: 8 * dimensions of graph
 network: 12 * dimensions of graph network: 16 ### DATA-SPECIFIC
 INFORMATION FOR: Prediction effects of key generic technologies.csv 1.
 Number of variables: 10 2. Number of cases/rows: 3 3. Variable List: *
 epochs: 100 * epochs: 200 * epochs: 300 * epochs: 400 * epochs: 500 *
 epochs: 600 * epochs: 700 * epochs: 800 * epochs: 900 * epochs: 1000