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首页> 外文期刊>Expert Systems with Application >Graph kernel based measure for evaluating the influence of patents in a patent citation network
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Graph kernel based measure for evaluating the influence of patents in a patent citation network

机译：基于图核的度量，用于评估专利引用网络中专利的影响

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摘要

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.
机译：确定重要专利有助于推动业务增长并集中投资。过去，诸如度中心度和中间度中心度之类的中心度措施已用于识别专利引用网络中有影响力或重要的专利。如何分析像技术变革这样的复杂过程是一个重要的研究课题。但是，目前还没有集中度度量可利用此功能强大的图形内核。基于图核后节点相似度矩阵的变化，提出了一种新的中心度度量。所提出的方法提供了对影响节点识别的更强大的理解，因为它通过考虑直接和间接的专利引用来关注图结构信息。这项研究从以下前提开始：由于删除了给定节点而导致的相似度矩阵的变化表明了该节点在其网络内的重要性，因为每个节点都对节点之间的相似度矩阵做出了贡献。在计算网络中该节点存在和不存在的情况下的奇异值之后，我们计算给定节点的相似性矩阵范数的变化。然后，在相似度矩阵范数中导致最大变化（即减小）的节点被认为是最有影响力的节点。我们使用人工数据和真实的美国专利数据将我们提出的方法与其他广泛使用的集中度测量方法的性能进行比较。实验结果表明，我们提出的方法比现有方法具有更好的性能。

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  • 来源
    《Expert Systems with Application》 |2015年第3期|1479-1486|共8页
  • 作者

    Andrew Rodriguez; Byunghoon Kim; Jae-Min Lee; Byoung-Yul Coh; Myong K. Jeong;

  • 作者单位

    Department of Industrial & Systems Engineering, Rutgers University, 96 Frelinghuysen Road, CoRE Building Room 201, Piscataway, NJ 08854, USA;

    Department of Industrial & Systems Engineering, Rutgers University, 96 Frelinghuysen Road, CoRE Building Room 201, Piscataway, NJ 08854, USA;

    Information Analysis Center, Korea Institute of Science and Technology Information, 66 Hoegiro, Dongdaemun-gu, Seoul 130-741, Republic of Korea;

    Information Analysis Center, Korea Institute of Science and Technology Information, 66 Hoegiro, Dongdaemun-gu, Seoul 130-741, Republic of Korea;

    Department of Industrial & Systems Engineering, Rutgers University, 96 Frelinghuysen Road, CoRE Building Room 201, Piscataway, NJ 08854, USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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

    机译：集中度度量;专利引用网络;图内核;相似度矩阵矩阵范数;

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