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The development of innovations in all the research and development (R&D) fields is leading to a huge increase of patent data. Therefore, it is reasonable to foresee that, in the next future, Big Data-centered techniques will be compulsory to fully exploit the potential of this kind of data. In this context, network analysis-based approaches are extremely promising. The purpose of this paper is to provide a contribution to this setting. In fact, the authors propose a well-tailored centrality measure for evaluating patents and their citations.

The authors preliminarily introduce a suitable support directed network representing patents and their citations. After this, the authors present the centrality measures, namely, “Naive Patent Degree” and “Refined Patent Degree.’” Then, the authors show why they are well tailored to capture the specificities of the patent scenario and why classical centrality measure fails to fully reach this purpose.

The authors present three possible applications of the measures, namely: the computation of a patent “scope” allowing the evaluation of the width and the strength of the influence of a patent on a given R&D field; the computation of a patent lifecycle; and the detection of the so-called “power patents,” i.e., the most relevant patents, and the investigation of the importance, for a patent, to be cited by a power patent.

None of the approaches proposing the application of centrality measures to patent citation networks consider the main peculiarity of this scenario, i.e., that, if a patent p_i cites a patent p_j, then the value of p_i decreases. So, differently from classical scientific paper citation scenario, in this one performing a citation has a cost for the citing entity. This fact is not considered by all the approaches conceived to investigate paper citations. Nevertheless, this feature represents the core of patent citation scenario. The approach has been explicitly conceived to capture this feature.