PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Analyzing the structure of U.S. patents network
Vladimir Batagelj, Nataša Kejžar, Simona Korenjak-Černe and Matjaž Zaveršnik
In: Data science and classification Studies in classification, data analysis, and . (2006) Springer , Berlin , pp. 141-148. ISBN 3-540-34415-2

Abstract

The U.S. patents network is a network of almost 3.8 millions patents (network vertices) from the year 1963 to 1999 (Hall et al., 2001) and more than 16.5 millions citations (network arcs). It is an example of a very large citation network. We analyzed the U.S. patents network with the tools of network analysis in order to get insight into the structure of the network as an initial step to the study of innovations and technical changes based on patents citation network data. In our approach the SPC (Search Path Count) weights, proposed by Hummon and Doreian (1989), for vertices and arcs are calculated first. Based on these weights vertex and line islands (Batagelj and Zaveršnik, 2004) are determined to identify the main themes of U.S. patents network. All analyses were done with Pajek - a program for analysis and visualization of large networks. As a result of the analysis the obtained main U.S. patents topics are presented.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:2854
Deposited By:Primož Lukšic
Deposited On:22 November 2006