Graph kernel based measure for evaluating the influence of patents in a patent citation network
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.
graph kernel patent citation network matrix norm centrality measure similarity matrix