Home
Resource
Graph mining algorithms for the analysis of patent citation networks
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(7.95 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Rodriguez, Andrew David. Graph mining algorithms for the analysis of patent citation networks. Retrieved from https://doi.org/doi:10.7282/T3H133VP

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitleGraph mining algorithms for the analysis of patent citation networks
NameRodriguez, Andrew David (author); Jeong, Myong K (chair); Li, Kang (internal member); Albin, Susan (internal member); Gong, Jie (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2015
Other Date2015-05 (degree)
SubjectIndustrial and Systems Engineering, Patent searching, Data mining--Graphic methods
Extent1 online resource (xv, 121 p. : ill.)
DescriptionPatent and patent citation networks are rich datasets. In this dissertation we develop graph mining algorithms for the analysis of patent citation networks. First we develop a measure of patent influence within a patent citation network. Identifying influential or important patents helps in decision making, including focusing investment. We propose algorithms based on the powerful graph kernels for the ranking of patents in influence, and we demonstrate how the von Neumann graph kernel is well suited for influence analysis in patent citation networks. Secondly, we present new similarity measures between patents in a patent citation network. In the past, techniques such as text mining and keyword analysis have been applied for patent similarity calculation. The drawback of these approaches is that they depend on word choice and writing styles of authors. In this work we develop new similarity measures for patents in a patent citation network using only the patent citation network structure. The proposed similarity measures use multi-stage co-citation and bibliographic coupling links. Applications of the similarity measures include outlier scoring of patents in patent citation networks. Finally, we propose new methods for scoring and ranking patents in outlierness within a patent citation dataset. A distinguishing characteristic of patent datasets is that they contain both attribute data describing patents, as well as graph structure data in the citation network. Traditional outlier ranking techniques usually focus on either homogeneous vector data or on graph structure data. In this work we propose new outlier ranking methods developed specifically for patents in an attributed patent citation network. One challenge is how outlier ranking should handle these two different data types in an integrated fashion. To address this challenge, we first develop a new patent subspace clustering algorithm that considers both types of data. Based on the patent clustering result, we then develop methods for the scoring and ranking of patents in outlierness within patent citation networks. Proposed outlier score functions consider both patent attribute data and graph structure data. We compare the performance of our developed approaches with existing approaches using synthetic data and real-life U.S. patent data.
NotePh.D.
NoteIncludes bibliographical references
Noteby Andrew David Rodriguez
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3H133VP
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024