Scale-Free Network Analysis of Big Data for Patent Litigation Cases in the United States

This study empirically analyzes the structure and behavior of the patent litigation network in the U.S. by introducing complex network theory characterized as growth and preferential attachment rules. In this study, we draw a log-log plot of the probability distribution for both the plaintiff and the defendant sides and use a log-transform regression to verify that the patent litigation network degree distribution follows a power-law distribution. We also graph the structure of the network to explore the origin of its asymmetrical pattern. In addition, we investigate the behavior of the patent litigation network over time by calculating the Shannon entropy for each year from 2005 to September 2016. We find the power-law degree distribution, and a few hubs of the patent litigation network are like other real-world networks. We also find that the asymmetrical pattern of the patent litigation network is largely driven by non-practicing entities and the major information technology firms. We conclude that the patent litigation network is becoming more asymmetrical over time based on our finding that its Shannon entropy is decreasing.