Community evolution in patent networks: technological change and network dynamics View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Yuan Gao, Zhen Zhu, Raja Kali, Massimo Riccaboni

ABSTRACT

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. More... »

PAGES

26

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s41109-018-0090-3

DOI

http://dx.doi.org/10.1007/s41109-018-0090-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106132096

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30839799


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