Early identification of important patents through network centrality
This addresses the challenge of technological forecasting for policymakers and analysts, though it is incremental as it builds on existing network analysis methods.
The paper tackled the problem of early identification of historically significant patents using the US patent citation network, showing that a rescaled PageRank measure based on network topology and temporal information identifies these patents earlier than citation counts or standard PageRank.
One of the most challenging problems in technological forecasting is to identify as early as possible those technologies that have the potential to lead to radical changes in our society. In this paper, we use the US patent citation network (1926-2010) to test our ability to early identify a list of historically significant patents through citation network analysis. We show that in order to effectively uncover these patents shortly after they are issued, we need to go beyond raw citation counts and take into account both the citation network topology and temporal information. In particular, an age-normalized measure of patent centrality, called rescaled PageRank, allows us to identify the significant patents earlier than citation count and PageRank score. In addition, we find that while high-impact patents tend to rely on other high-impact patents in a similar way as scientific papers, the patents' citation dynamics is significantly slower than that of papers, which makes the early identification of significant patents more challenging than that of significant papers.