Patent citation network analysis for the domain of organic photovoltaic cells: Country, institution, and technology field

The goal of this work is to understand the structure and characteristics of technological knowledge flows between countries, institutions, and technology fields in the field of organic photovoltaic cells. This study was conducted in three stages: data collection, network creation, and network analysis. For network analysis, network visualization, network topological analysis, and node centrality analysis were performed in sequence. The network topological analysis revealed that all three citation networks, i.e., countries, institutions, and technology fields, are scale-free networks that follow the power law and display, to a greater or lesser extent, a more efficient knowledge transfer capability than a random network of the same size. The node centrality analysis showed that the United States, Japan, and Germany are the most important citation centers in the country citation network, while Boeing, Konarka Technologies, Eastman Kodak, and Sharp are the most important in the institution citation network, and the U.S. patent classification (USPC) classes of 136, 257, and 428 are the most important in the technology field citation network, each playing critical roles in each the network as core nodes. In this study, we applied various concepts of centrality to the analysis of individual nodes and found that the results from the network topological analysis and the node centrality analysis are not significantly different. The proposed analysis framework in this paper is applicable to different science and technology domains.