Graph Traversal

Graph traversal is the process of visiting nodes in a graph data structure by following edges in a systematic way. The two fundamental traversal algorithms are breadth-first search (BFS), which explores all neighbors at the current depth before moving deeper, and depth-first search (DFS), which explores as far as possible along each branch before backtracking.

In knowledge graph systems, graph traversal is used to answer multi-hop queries -- questions that require following multiple relationships. For example, answering "What skills do Alice's teammates have?" requires traversing Alice -> team -> teammates -> skills, a multi-hop path through the graph. Cypher queries in Neo4j express traversals naturally using variable-length path patterns like (a)-[*1..3]-(b) for paths of 1 to 3 hops.

Graph traversal is a key differentiator of knowledge graphs over vector databases. While vector search finds semantically similar documents, graph traversal follows explicit structured relationships, enabling precise reasoning over entity connections that would be impossible with similarity search alone.