Abstract
The patent is an indicator of research and development output that can be quantitatively analyzed. A patent analysis can be utilized to evaluate the value of a firm's intangible assets. Up to now, patent-index methods have been widely adopted by various researchers for patent-related analyses. Unlike those, this study attempts to use the social network concept and small-world network behavior to analyze the TFT-LCD patent citation networks. The study shows that the patent citation network can be fully characterized by using the small-world model. We also analyze the number of upstream and downstream citations and discover that the number of patent citations is distributed according to the power law. When investigating patent citation growth, we also notice that patent citations are highly selective. In other words, only a few patents represent the mainstream of industry development. Finally, we validate the importance of patents with high betweenness centrality and discover that 63.75% of patent citations are closely related to them. When the patents with high betweenness centrality are removed from the citation network, 50.6% of patent technical information ceases to flow.
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