Patent citation network analysis: Topology and evolution of patent citation networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The network of patents connected by citations is an evolving graph, which represents the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. We review models evolution of the patent citation network both at the “microscopic” level of individual patents [1–5] and at “mesoscopic” [6] levels. Microscopic level studies helped to measure the “attractivness” of a patent, as the function of its age and the number of citation already has obtained. A somewhat similar approach was given here: [7]. At mesoscopic level the analysis has been extended to subclasses, and it was demonstrated by adopting clustering algorithms that it is possible to detect and predict emerging new technology clusters.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings
PublisherSpringer Verlag
Pages543
Number of pages1
Volume9886 LNCS
ISBN (Print)9783319447773
Publication statusPublished - 2016
Event25th International Conference on Artificial Neural Networks, ICANN 2016 - Barcelona, Spain
Duration: Sep 6 2016Sep 9 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9886 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other25th International Conference on Artificial Neural Networks, ICANN 2016
CountrySpain
CityBarcelona
Period9/6/169/9/16

Fingerprint

Citation Analysis
Patents
Citations
Network Analysis
Electric network analysis
Clustering algorithms
Innovation
Topology
Clustering Algorithm
Imply
Predict
Graph in graph theory

Keywords

  • Evolving networks
  • Patent citation analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Érdi, P. (2016). Patent citation network analysis: Topology and evolution of patent citation networks. In Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings (Vol. 9886 LNCS, pp. 543). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9886 LNCS). Springer Verlag.

Patent citation network analysis : Topology and evolution of patent citation networks. / Érdi, P.

Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings. Vol. 9886 LNCS Springer Verlag, 2016. p. 543 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9886 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Érdi, P 2016, Patent citation network analysis: Topology and evolution of patent citation networks. in Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings. vol. 9886 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9886 LNCS, Springer Verlag, pp. 543, 25th International Conference on Artificial Neural Networks, ICANN 2016, Barcelona, Spain, 9/6/16.
Érdi P. Patent citation network analysis: Topology and evolution of patent citation networks. In Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings. Vol. 9886 LNCS. Springer Verlag. 2016. p. 543. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Érdi, P. / Patent citation network analysis : Topology and evolution of patent citation networks. Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings. Vol. 9886 LNCS Springer Verlag, 2016. pp. 543 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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