Graph Traversal
Graph traversal is the process of systematically visiting the nodes of a graph according to some rule so that the structure can be explored, searched, or analyzed.
The two canonical traversal styles are breadth-first search and depth-first search.
- Breadth-first traversal explores outward layer by layer.
- Depth-first traversal pushes down one branch as far as possible before backing up.
Why the distinction matters
The difference is not cosmetic. Each traversal exposes a different shape of the graph.
Breadth-first traversal is naturally tied to distance in unweighted graphs, because it discovers all nodes one edge away before nodes two edges away, and so on. Depth-first traversal is often better for exhaustively exploring structure, recursive decomposition, or tasks where following a branch to completion is useful.
In introduction-to-algorithms-and-machine-learning, graph traversal marks a conceptual shift. The learner moves from arrays, matrices, and tabular data into explicitly relational structure. That matters because many real problems are not best understood as rows in a table. They are networks.
Why it belongs near search
Graph traversal is really a form of structured search. It asks not only whether something can be found, but how movement through the space should be organized. That places it near backtracking, though the two are not the same. Traversal explores a known graph systematically; backtracking builds and prunes a search tree while solving a constraint problem.
Limits
Traversal by itself does not tell you what the graph means. It is a way of moving through structure, not a complete interpretation of that structure.