Graph databases are a good fit for applications with high transactional use cases, such as personalized recommendations, fraud detection, and network/IT management. These applications benefit from the ability to traverse complex relationships between entities in real-time, which is often more efficient than using relational databases. Graph databases offer advantages when dealing with entities connected by multiple relationships, variable-length graph traversals, and pattern recognition. However, they may not be as performant for queries that require aggregations or calculations on large datasets, such as calculating average heights of actors in a database. The key to using graph databases effectively is understanding their strengths and limitations and choosing the right data model for your specific use case.