Graph kernel based measure for evaluating the influence of patents in a patent citation network. Issue 3 (15th February 2015) - Rodriguez, Andrew; Kim, Byunghoon; Lee, Jae-Min; Coh, Byoung-Yul; Jeong, Myong K.
Highlights: A new kernel based influence measure for evaluating patent influence is proposed. We use the difference in kernel matrix norms as a measure of node influence. Node with largest difference in matrix norm is considered as most influential node. Von Neumann kernel can be used to account for both direct and indirect citations. Experiments show that our proposed approach performs better than existing measures. 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.