Graph kernel based measure for evaluating the influence of patents in a patent citation network

Andrew Rodriguez, Byunghoon Kim, Jae Min Lee, Byoung Yul Coh, Myong-Kee Jeong

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Identifying important patents helps to drive business growth and focus investment. In the past, centrality measures such as degree centrality and betweenness centrality have been applied to identify influential or important patents in patent citation networks. How such a complex process like technological change can be analyzed is an important research topic. However, no existing centrality measure leverages the powerful graph kernels for this end. This paper presents a new centrality measure based on the change of the node similarity matrix after leveraging graph kernels. The proposed approach provides a more robust understanding of the identification of influential nodes, since it focuses on graph structure information by considering direct and indirect patent citations. This study begins with the premise that the change of similarity matrix that results from removing a given node indicates the importance of the node within its network, since each node makes a contribution to the similarity matrix among nodes. We calculate the change of the similarity matrix norms for a given node after we calculate the singular values for the case of the existence and the case of nonexistence of that node within the network. Then, the node resulting in the largest change (i.e.; decrease) in the similarity matrix norm is considered to be the most influential node. We compare the performance of our proposed approach with other widely-used centrality measures using artificial data and real-life U.S. patent data. Experimental results show that our proposed approach performs better than existing methods.

Original languageEnglish (US)
Pages (from-to)1479-1486
Number of pages8
JournalExpert Systems With Applications
Volume42
Issue number3
DOIs
StatePublished - Feb 15 2015

Fingerprint

Industry

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Keywords

  • Centrality measure
  • Graph kernel
  • Matrix norm
  • Patent citation network
  • Similarity matrix

Cite this

Rodriguez, Andrew ; Kim, Byunghoon ; Lee, Jae Min ; Coh, Byoung Yul ; Jeong, Myong-Kee. / Graph kernel based measure for evaluating the influence of patents in a patent citation network. In: Expert Systems With Applications. 2015 ; Vol. 42, No. 3. pp. 1479-1486.
@article{f1c5190f86264f4da35fe81be0540f54,
title = "Graph kernel based measure for evaluating the influence of patents in a patent citation network",
abstract = "Identifying important patents helps to drive business growth and focus investment. In the past, centrality measures such as degree centrality and betweenness centrality have been applied to identify influential or important patents in patent citation networks. How such a complex process like technological change can be analyzed is an important research topic. However, no existing centrality measure leverages the powerful graph kernels for this end. This paper presents a new centrality measure based on the change of the node similarity matrix after leveraging graph kernels. The proposed approach provides a more robust understanding of the identification of influential nodes, since it focuses on graph structure information by considering direct and indirect patent citations. This study begins with the premise that the change of similarity matrix that results from removing a given node indicates the importance of the node within its network, since each node makes a contribution to the similarity matrix among nodes. We calculate the change of the similarity matrix norms for a given node after we calculate the singular values for the case of the existence and the case of nonexistence of that node within the network. Then, the node resulting in the largest change (i.e.; decrease) in the similarity matrix norm is considered to be the most influential node. We compare the performance of our proposed approach with other widely-used centrality measures using artificial data and real-life U.S. patent data. Experimental results show that our proposed approach performs better than existing methods.",
keywords = "Centrality measure, Graph kernel, Matrix norm, Patent citation network, Similarity matrix",
author = "Andrew Rodriguez and Byunghoon Kim and Lee, {Jae Min} and Coh, {Byoung Yul} and Myong-Kee Jeong",
year = "2015",
month = "2",
day = "15",
doi = "10.1016/j.eswa.2014.08.051",
language = "English (US)",
volume = "42",
pages = "1479--1486",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",
number = "3",

}

Graph kernel based measure for evaluating the influence of patents in a patent citation network. / Rodriguez, Andrew; Kim, Byunghoon; Lee, Jae Min; Coh, Byoung Yul; Jeong, Myong-Kee.

In: Expert Systems With Applications, Vol. 42, No. 3, 15.02.2015, p. 1479-1486.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Graph kernel based measure for evaluating the influence of patents in a patent citation network

AU - Rodriguez, Andrew

AU - Kim, Byunghoon

AU - Lee, Jae Min

AU - Coh, Byoung Yul

AU - Jeong, Myong-Kee

PY - 2015/2/15

Y1 - 2015/2/15

N2 - Identifying important patents helps to drive business growth and focus investment. In the past, centrality measures such as degree centrality and betweenness centrality have been applied to identify influential or important patents in patent citation networks. How such a complex process like technological change can be analyzed is an important research topic. However, no existing centrality measure leverages the powerful graph kernels for this end. This paper presents a new centrality measure based on the change of the node similarity matrix after leveraging graph kernels. The proposed approach provides a more robust understanding of the identification of influential nodes, since it focuses on graph structure information by considering direct and indirect patent citations. This study begins with the premise that the change of similarity matrix that results from removing a given node indicates the importance of the node within its network, since each node makes a contribution to the similarity matrix among nodes. We calculate the change of the similarity matrix norms for a given node after we calculate the singular values for the case of the existence and the case of nonexistence of that node within the network. Then, the node resulting in the largest change (i.e.; decrease) in the similarity matrix norm is considered to be the most influential node. We compare the performance of our proposed approach with other widely-used centrality measures using artificial data and real-life U.S. patent data. Experimental results show that our proposed approach performs better than existing methods.

AB - Identifying important patents helps to drive business growth and focus investment. In the past, centrality measures such as degree centrality and betweenness centrality have been applied to identify influential or important patents in patent citation networks. How such a complex process like technological change can be analyzed is an important research topic. However, no existing centrality measure leverages the powerful graph kernels for this end. This paper presents a new centrality measure based on the change of the node similarity matrix after leveraging graph kernels. The proposed approach provides a more robust understanding of the identification of influential nodes, since it focuses on graph structure information by considering direct and indirect patent citations. This study begins with the premise that the change of similarity matrix that results from removing a given node indicates the importance of the node within its network, since each node makes a contribution to the similarity matrix among nodes. We calculate the change of the similarity matrix norms for a given node after we calculate the singular values for the case of the existence and the case of nonexistence of that node within the network. Then, the node resulting in the largest change (i.e.; decrease) in the similarity matrix norm is considered to be the most influential node. We compare the performance of our proposed approach with other widely-used centrality measures using artificial data and real-life U.S. patent data. Experimental results show that our proposed approach performs better than existing methods.

KW - Centrality measure

KW - Graph kernel

KW - Matrix norm

KW - Patent citation network

KW - Similarity matrix

UR - http://www.scopus.com/inward/record.url?scp=84908405826&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84908405826&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2014.08.051

DO - 10.1016/j.eswa.2014.08.051

M3 - Article

AN - SCOPUS:84908405826

VL - 42

SP - 1479

EP - 1486

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 3

ER -