Ranking Algorithms: Application for Patent Citation Network

How do technologies evolve in time? One way of answering this is by studying the US patent citation network. We begin this exploration by looking at macroscopic temporal behavior of classes of patents. Next, we quantify the influence of a patent by examining two major methods of ranking of nodes in networks: the celebrated “PageRank” and one of its extensions, reinforcement learning. A short history and a detailed explanation of the algorithms are given. We also discuss the influence of the damping factor when using PageRank on the patent network specifically in the context of rank reversal. These algorithms can be used to give more insight into the dynamics of the patent citation network. Finally, we provide a case study which combines the use of clustering algorithms with ranking algorithms to show the emergence of the opioid crisis. There is a great deal of data contained within the patent citation network. Our work enhances the usefulness of this data, which represents one of the important information quality characteristics. We do this by focusing on the structure and dynamics of the patent network, which allows us to determine the importance of individual patents without using any information about the patent except the citations to and from the patent.