Ranking Algorithms: Application for Patent Citation Network

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.

[1]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[2]  S. Fortunato,et al.  Spectral centrality measures in complex networks. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Herman Joseph,et al.  Opioids and the treatment of chronic pain: controversies, current status, and future directions. , 2008, Experimental and clinical psychopharmacology.

[4]  Péter Érdi,et al.  Prediction of emerging technologies based on analysis of the US patent citation network , 2012, Scientometrics.

[5]  Peter Grassberger,et al.  PageRank and rank-reversal dependence on the damping factor , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Péter Érdi,et al.  Recognition of emerging technology trends: class-selective study of citations in the U.S. Patent Citation Network , 2016, Scientometrics.

[7]  David F. Gleich,et al.  PageRank beyond the Web , 2014, SIAM Rev..

[8]  Sergei Maslov,et al.  Promise and Pitfalls of Extending Google's PageRank Algorithm to Citation Networks , 2008, The Journal of Neuroscience.

[9]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[10]  Enoch Peserico,et al.  Choose the damping, choose the ranking? , 2010, J. Discrete Algorithms.

[11]  Ilse C. F. Ipsen,et al.  PageRank Computation, with Special Attention to Dangling Nodes , 2007, SIAM J. Matrix Anal. Appl..

[12]  Péter Érdi,et al.  Patent Citation Networks Revisited: Signs of a Twenty-First Century Change? , 2009 .

[13]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[14]  Francesco Romani,et al.  EVALUATING SCIENTIFIC PRODUCTS BY MEANS OF CITATION-BASED MODELS: A FIRST ANALYSIS AND VALIDATION ∗ , 2008 .

[15]  Vali Derhami,et al.  Applying reinforcement learning for web pages ranking algorithms , 2013, Appl. Soft Comput..

[16]  Peter ErdiKinga.  Prediction of emerging technologies based on analysis of the US patent citation network , 2013 .

[17]  Péter Érdi,et al.  Complexity Explained , 2006 .

[18]  Paolo Boldi.  TotalRank: ranking without damping , 2005, WWW '05.

[19]  Péter Érdi,et al.  The Inverse Problem of Evolving Networks — with Application to Social Nets , 2008 .

[20]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[21]  Jan Tobochnik,et al.  Modeling innovation by a kinetic description of the patent citation system , 2005 .

[22]  A. Barabasi,et al.  Social group dynamics in networks , 2009 .

[23]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.