Patent citation network analysis: Ranking: From web pages to patents

P. Érdi, Péter Bruck

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

1 Citation (Scopus)

Abstract

Ranking of nodes in a network of diverse number of connections (degree) is an extensively studied field. In the theory of social networks centrality measures were constructed to rank nodes of networks based on their (not unique) topological importance, Another family of measures is related to the spectral properties of the adjacency matrix [1], which takes into account the importance of the influence of a neighbor. Importance can be defined recursively. Brin and Page [2] introduced a matching recursive centrality measure called PageRank. The relevance of this algorithm to citation networks was expressed by [3]. By adopting a citation-based recursive ranking method for patents the evolution of new field of technologies can be traced. Specifically, the laser/inkjet printer technology emerged from the recombination of existing technologies, such as sequential printing and static image production. The dynamics of the citations coming from the different precursor classes illuminate the mechanism of the emergence of new fields and give the possibility to make predictions about future technological development [4]. The combination of using clustering algorithms with ranking algorithms give more insight about the dynamics of the patent citation network [5].

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings
PublisherSpringer Verlag
Pages544
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

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Keywords

  • PageRank
  • Patent citation analysis
  • Ranking

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Érdi, P., & Bruck, P. (2016). Patent citation network analysis: Ranking: From web pages to patents. In Artificial Neural Networks and Machine Learning - 25th International Conference on Artificial Neural Networks, ICANN 2016, Proceedings (Vol. 9886 LNCS, pp. 544). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9886 LNCS). Springer Verlag.