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Community evolution in patent networks: technological change and network dynamics
When studying patent data as a way to understand innovation and technological change, the conventional indicators might fall short, and categorizing technologies based on the existing classification systems used by patent authorities could cause inaccuracy and misclassification, as shown in literatu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214301/ https://www.ncbi.nlm.nih.gov/pubmed/30839799 http://dx.doi.org/10.1007/s41109-018-0090-3 |
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author | Gao, Yuan Zhu, Zhen Kali, Raja Riccaboni, Massimo |
author_facet | Gao, Yuan Zhu, Zhen Kali, Raja Riccaboni, Massimo |
author_sort | Gao, Yuan |
collection | PubMed |
description | When studying patent data as a way to understand innovation and technological change, the conventional indicators might fall short, and categorizing technologies based on the existing classification systems used by patent authorities could cause inaccuracy and misclassification, as shown in literature. Gao et al. (International Workshop on Complex Networks and their Applications, 2017) have established a method to analyze patent classes of similar technologies as network communities. In this paper, we adopt the stabilized Louvain method for network community detection to improve consistency and stability. Incorporating the overlapping community mapping algorithm, we also develop a new method to identify the central nodes based on the temporal evolution of the network structure and track the changes of communities over time. A case study of Germany’s patent data is used to demonstrate and verify the application of the method and the results. Compared to the non-network metrics and conventional network measures, we offer a heuristic approach with a dynamic view and more stable results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s41109-018-0090-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6214301 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-62143012018-11-13 Community evolution in patent networks: technological change and network dynamics Gao, Yuan Zhu, Zhen Kali, Raja Riccaboni, Massimo Appl Netw Sci Research When studying patent data as a way to understand innovation and technological change, the conventional indicators might fall short, and categorizing technologies based on the existing classification systems used by patent authorities could cause inaccuracy and misclassification, as shown in literature. Gao et al. (International Workshop on Complex Networks and their Applications, 2017) have established a method to analyze patent classes of similar technologies as network communities. In this paper, we adopt the stabilized Louvain method for network community detection to improve consistency and stability. Incorporating the overlapping community mapping algorithm, we also develop a new method to identify the central nodes based on the temporal evolution of the network structure and track the changes of communities over time. A case study of Germany’s patent data is used to demonstrate and verify the application of the method and the results. Compared to the non-network metrics and conventional network measures, we offer a heuristic approach with a dynamic view and more stable results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s41109-018-0090-3) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-08-13 2018 /pmc/articles/PMC6214301/ /pubmed/30839799 http://dx.doi.org/10.1007/s41109-018-0090-3 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Gao, Yuan Zhu, Zhen Kali, Raja Riccaboni, Massimo Community evolution in patent networks: technological change and network dynamics |
title | Community evolution in patent networks: technological change and network dynamics |
title_full | Community evolution in patent networks: technological change and network dynamics |
title_fullStr | Community evolution in patent networks: technological change and network dynamics |
title_full_unstemmed | Community evolution in patent networks: technological change and network dynamics |
title_short | Community evolution in patent networks: technological change and network dynamics |
title_sort | community evolution in patent networks: technological change and network dynamics |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214301/ https://www.ncbi.nlm.nih.gov/pubmed/30839799 http://dx.doi.org/10.1007/s41109-018-0090-3 |
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