From ranking and clustering of evolving networks to patent citation analysis

Hayley Beltz, Aniko Fulop, Raoul R. Wadhwa, P. Érdi

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

1 Citation (Scopus)

Abstract

The network of patents connected by citations is an evolving graph that represents the innovation process of society. A patent citing another implies that the cited patent contains a piece of previously existing knowledge that the citing patent is building upon. Understanding the development of the patent citation network contributes to the discovery of the rules that govern its growth. By adopting a citation-based recursive ranking method for patents, the evolution of new fields of technology can be traced. Specifically, a reinforcement learning based ranking algorithm was adopted and found more appropriate than the now classical PageRank algorithm. The temporal evolution of patent classes and the eventual interaction among them were studied by combining regression and clustering methods. While some patterns for the network dynamics have clearly been identified, more work is needed to see the details and to be able to make predictions for the emerging fields of technologies.

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1388-1394
Number of pages7
Volume2017-May
ISBN (Electronic)9781509061815
DOIs
Publication statusPublished - Jun 30 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: May 14 2017May 19 2017

Other

Other2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period5/14/175/19/17

Fingerprint

Reinforcement learning
Innovation

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Beltz, H., Fulop, A., Wadhwa, R. R., & Érdi, P. (2017). From ranking and clustering of evolving networks to patent citation analysis. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings (Vol. 2017-May, pp. 1388-1394). [7966015] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2017.7966015

From ranking and clustering of evolving networks to patent citation analysis. / Beltz, Hayley; Fulop, Aniko; Wadhwa, Raoul R.; Érdi, P.

2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Vol. 2017-May Institute of Electrical and Electronics Engineers Inc., 2017. p. 1388-1394 7966015.

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

Beltz, H, Fulop, A, Wadhwa, RR & Érdi, P 2017, From ranking and clustering of evolving networks to patent citation analysis. in 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. vol. 2017-May, 7966015, Institute of Electrical and Electronics Engineers Inc., pp. 1388-1394, 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, United States, 5/14/17. https://doi.org/10.1109/IJCNN.2017.7966015
Beltz H, Fulop A, Wadhwa RR, Érdi P. From ranking and clustering of evolving networks to patent citation analysis. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Vol. 2017-May. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1388-1394. 7966015 https://doi.org/10.1109/IJCNN.2017.7966015
Beltz, Hayley ; Fulop, Aniko ; Wadhwa, Raoul R. ; Érdi, P. / From ranking and clustering of evolving networks to patent citation analysis. 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Vol. 2017-May Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1388-1394
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