Graph databases offer a more intuitive and flexible way to represent and work with complex data structures compared to traditional relational databases. By using native graph database models, developers can focus on modeling the problem domain rather than worrying about how to represent it in a table-based system. Key benefits of graph databases include their ability to handle dynamic and complex data structures, as well as their performance advantages over relational systems. The examples provided demonstrate how to traverse graphs, find paths between nodes, and perform feature matching operations using Neo4j, a popular native graph database. These techniques can be applied in various domains, including social networks, collaborative filtering, and recommendation systems, where complex relationships between data entities are prevalent. By adopting a graph database approach, developers can unlock new functionality and performance capabilities that are not available in traditional relational databases.