What is a graph traversal in a graph database?

What is a graph traversal in a graph database? Graph traversal in a graph database is the process of navigating through connected nodes and edges to retrieve or analyze data. Unlike relational databases, which rely on tables and joins, graph databases store data as nodes (entities) and edges (relationships). Traversal allows developers to explore these connections efficiently, often for tasks like finding paths, discovering relationships, or aggregating data across linked nodes. For example, in a social network, traversal could identify all friends of a user or detect indirect connections between two people.

Traversal operations are typically powered by algorithms like depth-first search (DFS) or breadth-first search (BFS), which determine the order in which nodes are visited. In graph databases, these algorithms are optimized for speed, even with large datasets. For instance, DFS might explore a single branch of connections deeply (e.g., tracking a product’s supply chain hierarchy), while BFS could prioritize immediate connections (e.g., finding all users within two degrees of separation). Most graph databases, such as Neo4j or Amazon Neptune, provide built-in traversal methods or query languages (e.g., Cypher, Gremlin) to simplify this process.

Example and Use Cases A practical example is recommendation systems. Suppose a user buys a product in an e-commerce graph. Traversal could find other products bought by users who purchased the same item, navigating edges like BOUGHT_WITH or ALSO_VIEWED. In Cypher (Neo4j’s query language), this might look like:

MATCH (p:Product {id: '123'})<-[:BOUGHT]-(u:User)-[:BOUGHT]->(rec:Product)
RETURN rec 

This query traverses from the product to users who bought it, then to other products those users purchased. Another example is fraud detection: traversing transaction paths to identify suspicious patterns, such as accounts connected through multiple intermediaries. By leveraging traversal, developers avoid complex joins and perform these tasks in near-real time, making graph databases ideal for interconnected data scenarios.