Graph kernel based measure for evaluating the influence of patents in a patent citation network. Issue 3 (15th February 2015)

• Record Type: Journal Article
• Authors: Rodriguez, Andrew; Kim, Byunghoon; Lee, Jae-Min; Coh, Byoung-Yul; Jeong, Myong K.
• Is Part Of: Expert systems with applications. Volume 42:Issue 3(2015)
• Page Start: 1479
• Page End: 1486
• Publication Date: 2015-02-15
• Subjects: Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.08.051
• Languages: English
• ISSNs: 0957-4174

Abstract

<abstract xml:lang="en" abstract-type="author-highlights" id="ab005"> <title id="st120">Highlights</title> <sec> <p id="sp005"> <list id="l0005"> <list-item id="u0005"> <label></label> <p id="p0245">A new kernel based influence measure for evaluating patent influence is proposed.</p> </list-item> <list-item id="u0010"> <label></label> <p id="p0250">We use the difference in kernel matrix norms as a measure of node influence.</p> </list-item> <list-item id="u0015"> <label></label> <p id="p0255">Node with largest difference in matrix norm is considered as most influential node.</p> </list-item> <list-item id="u0020"> <label></label> <p id="p0260">Von Neumann kernel can be used to account for both direct and indirect citations.</p> </list-item> <list-item id="u0025"> <label></label> <p id="p0265">Experiments show that our proposed approach performs better than existing measures.</p> </list-item> </list> </p> </sec> </abstract>