Graph analytics has become a valuable tool in economics to better understand patent data and the knowledge economy. Studying the knowledge economy helps us explore how inventors interact with each other over time to produce new ideas and innovation. Patent data is used as a graph, which allows for natural and contextual relationships to be understood, making it easier to study complex phenomena. The graph schema has 13 million nodes and 110 million relationships, representing patents, inventors, locations, assignees, and self-relationships. Citation trees are built using Neo4j Bloom, showing how knowledge develops over time, with an exponential increase in patents as the number of inventors increases. Collaboration networks, where collaboration leads to technological growth, are represented by a star-shaped graph, while frictions to collaboration, such as individual inventor behavior and success influencing collaboration, are also studied using patent data. The analysis reveals insights into the dynamics of innovation and the factors that influence it, highlighting the richness of patent data and the importance of graph analytics in understanding the knowledge economy.