random_walk

Generic Description

Emit random-walk traversal sequences.

Simple example:

CALL nexyron.random_walk()

Similarity and embedding procedures compare nodes by neighborhood overlap, random-walk context, or learned vector position. They are useful for link prediction, recommendations, deduplication, and candidate generation.

Consumer-Level Explanation

Use random_walk when emit random-walk traversal sequences is the graph-analysis or operational question you actually need to answer. Keep the call explicit because its YIELD columns, row grain, and required runtime state determine how the rest of the Cypher pipeline can safely compose it. Structural similarity and embedding similarity are not the same: overlap metrics are explainable and local, while embeddings can capture broader context but need downstream validation.

Conceptual Explanation

random_walk should be read as one named procedure contract in the broader Nexyron Cypher surface. The procedure call is not only a syntax hook: it defines what runtime state, projection, registry entry, or graph algorithm is being asked to operate, and it determines which output columns downstream YIELD, WITH, and RETURN stages can safely use.

More Detailed Explanation

Random walks differ from deterministic traversals by representing stochastic exploration useful in graph learning or simulation

In practical queries, start by deciding the row grain you want after the call: one row per node, one row per path, one row per registry object, one row per artifact, or one row per summary. Then keep that grain explicit with YIELD and named projections. That is the difference between a useful planner-facing example and a vague call that downstream tooling cannot safely compose. For contract-driven procedures, the executable examples on these pages intentionally inspect procedure metadata unless the required named artifacts are created in the same example.

How It Differs From Nearby Algorithms

Random walks differ from deterministic traversals by representing stochastic exploration useful in graph learning or simulation.

Advanced Example

This example yields named columns from random_walk and returns a bounded result shape that downstream planner tooling can compose without guessing column names or row grain.

CALL nexyron.random_walk() YIELD walk_id, step, node_id
RETURN walk_id, step, node_id
LIMIT 10

Real Use Cases

Real Limitations And Tradeoffs