Advertisement

Ranking Algorithms: Application for Patent Citation Network

  • Hayley Beltz
  • Timothy Rutledge
  • Raoul R. Wadhwa
  • Péter Bruck
  • Jan Tobochnik
  • Anikó Fülöp
  • György Fenyvesi
  • Péter ÉrdiEmail author
Chapter
Part of the Information Fusion and Data Science book series (IFDS)

Abstract

How do technologies evolve in time? One way of answering this is by studying the US patent citation network. We begin this exploration by looking at macroscopic temporal behavior of classes of patents. Next, we quantify the influence of a patent by examining two major methods of ranking of nodes in networks: the celebrated “PageRank” and one of its extensions, reinforcement learning. A short history and a detailed explanation of the algorithms are given. We also discuss the influence of the damping factor when using PageRank on the patent network specifically in the context of rank reversal. These algorithms can be used to give more insight into the dynamics of the patent citation network. Finally, we provide a case study which combines the use of clustering algorithms with ranking algorithms to show the emergence of the opioid crisis. There is a great deal of data contained within the patent citation network. Our work enhances the usefulness of this data, which represents one of the important information quality characteristics. We do this by focusing on the structure and dynamics of the patent network, which allows us to determine the importance of individual patents without using any information about the patent except the citations to and from the patent.

Keywords

Ranking Algorithms PageRank Reinforcement learning Patents Clustering 

Notes

Acknowledgements

PE thanks the Henry Luce Foundation for support of Complex Systems Studies as Henry R Luce Professor. JT thanks the Herbert H. and Grace A. Dow Foundation for support as the Dow Distinguished Professor of the Natural Sciences.

References

  1. 1.
    V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)Google Scholar
  2. 2.
    P. Boldi, Totalrank: ranking without damping, in 14th International World Wide Web Conference (WWW2005) (ACM Press, New York, 2005), pp. 898–899Google Scholar
  3. 3.
    M. Bressan, E. Peserico, Choose the damping, choose the ranking? J. Discre. Algorithms 8(2), 199–213 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    S. Brin, L. Page, The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)CrossRefGoogle Scholar
  5. 5.
    P. Bruck, I. Réthy, J. Szente, J. Tobochnik, P. Érdi, Recognition of emerging technology trends: class-selective study of citations in the U.S. patention citation network. Scientometrics 107(3), 1465–1475 (2016)Google Scholar
  6. 6.
    Y. Choe (ed.), in From Ranking and Clustering of Evolving Networks to Patent Citation Analysis, Anchorage (IEEE Computational Intelligence Society, 2017)Google Scholar
  7. 7.
    G. Csardi, T. Nepusz, The igraph software package for complex network research. InterJournal Comp. Syst. 1695, 1–9 (2006)Google Scholar
  8. 8.
    G. Csárdi, K.J. Strandburg, L. Zalányi, J. Tobochnik, P. Érdi, Modeling innovation by a kinetic description of the patent citation system. Phys. A Stat. Mech. Appl. 374(2), 783–793 (2007)CrossRefGoogle Scholar
  9. 9.
    G. Csárdi, K.J. Strandburg, J. Tobochnik, P. Érdi, The inverse problem of evolving networks – with application to social nets, in Handbook of Large-Scale Random Networks, ed. by B. Bollobás, R. Kozma, D. Miklós (Springer, Berlin/Heidelberg, 2008), chap. 10, pp. 409–443Google Scholar
  10. 10.
    D.A. Bini, G.M. del Corso, F. Romani, Evaluating scientific products by means of citaiton-based models: A first analysis and validation. Electron. Trans. Numer. Anal. 33, 1–16 (2008)MathSciNetzbMATHGoogle Scholar
  11. 11.
    V. Derhami, E. Khodadadian, M. Ghasemzadeh, A.M.Z. Bidoki, Applying reinforcement learning for web pages ranking algorithms. Appl. Soft Comput. 13(4), 1686–1692 (2013)CrossRefGoogle Scholar
  12. 12.
    P. Érdi, Complexity Explained. Springer Complexity (Springer, New York, 2008)zbMATHGoogle Scholar
  13. 13.
    P. Érdi, K. Makovi, Z. Somogyvári, K. Strandburg, J. Tobochnik, P. Volf, L. Zalányi, Prediction of emerging technologies based on analysis of the US patent citation network. Scientometrics 95(1), 225–242 (2013)CrossRefGoogle Scholar
  14. 14.
    D.F. Gleich, PageRank beyond the Web. SIAM Rev. 57(3), 321–363 (2015). https://epubs.siam.org/doi/abs/10.1137/140976649 MathSciNetCrossRefGoogle Scholar
  15. 15.
    S. Maslov, S. Redner, Promise and pitfalls of extending googles pagerank algorithm to citation networks. J. Neurosci. 29, 1103–1105Google Scholar
  16. 16.
    L. Page, S. Brin, R. Motwani, T. Winograd, The pagerank citation ranking: bringing order to the web. Stanford InfoLab (1999)Google Scholar
  17. 17.
    G. Palla, P. Pollner, A.-L. Barabási, T. Vicsek, Social group dynamics in networks, in Adaptive Networks: Theory, Models and Applications, ed. by T. Gross, H. Sayama (Springer, Berlin/Heidelberg, 2009), chap. 2, pp. 11–38Google Scholar
  18. 18.
    N. Perra, S. Fortunato, Spectral centrality measures in complex network. Phys. Rev. 78, 036107 (2008)MathSciNetGoogle Scholar
  19. 19.
    R Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017)Google Scholar
  20. 20.
    A. Rosenblum, L. Marsch, H. Joseph, R. Porteno, Opioids and the treatment of chronic pain: controversies, current status, and future directions. Exp. Clin. Psychopharmacol. 16(5), 405–416 (2008)CrossRefGoogle Scholar
  21. 21.
    S.-W. Son, C. Christensen., P. Grassberger, M. Paczuski, Pagerank and rank-reversal dependence on the damping factor. Phys. Rev. E 86, 066104 (2012)Google Scholar
  22. 22.
    K.J. Strandburg, G. Csárdi, J. Tobochnik, P. Érdi, L. Zálanyi, Law and the science of networks: an overview and an application to the ‘patent explosion’. Berkeley Technol. Law J. 21, 1293 (2007)Google Scholar
  23. 23.
    K.J. Strandburg, G. Csárdi, J. Tobochnik, P. Érdi, Patent citation networks revisited: signs of a twenty-first century change. North Carolina Law Rev. 87(5), 1657–1698 (2009)Google Scholar
  24. 24.
    R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction (The MIT Press, London, 1998)zbMATHGoogle Scholar
  25. 25.
    WIPO, International Patent Classification(IPC) (2018)Google Scholar
  26. 26.
    I.C.F. Ipsen, T.M. Selee, PageRank computation, with special attention to dangling nodes. SIAM J. Matrix Anal. Appl. 29(4), 1281–1296 (2008). https://epubs.siam.org/doi/abs/10.1137/060664331 MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hayley Beltz
    • 1
  • Timothy Rutledge
    • 1
  • Raoul R. Wadhwa
    • 1
  • Péter Bruck
    • 2
  • Jan Tobochnik
    • 3
  • Anikó Fülöp
    • 4
  • György Fenyvesi
    • 5
  • Péter Érdi
    • 1
    Email author
  1. 1.Center for Complex SystemsKalamazoo CollegeKalamazooUSA
  2. 2.ProcessExpert LtdBudapestHungary
  3. 3.Department of PhysicsKalamazoo CollegeKalamazooUSA
  4. 4.Wigner Research Centre for PhysicsHungarian Academy of SciencesBudapestHungary
  5. 5.Poliphon Ltd.BudapestHungary

Personalised recommendations