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 K. Jeong

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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
Issue number3
StatePublished - Feb 15 2015

All Science Journal Classification (ASJC) codes

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


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


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