Graph kernel based measure for evaluating the influence of patents in a patent citation network
作者：
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.
摘要
•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.
论文关键词：Centrality measure,Patent citation network,Graph kernel,Similarity matrix,Matrix norm
论文评审过程：Available online 22 September 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.08.051
