clustering_coefficient

Generic Description

Measure local triadic closure around nodes.

Simple example:

CALL nexyron.clustering_coefficient()

Clustering and triangle procedures measure local closure: whether neighbors of a node also connect to each other. They are useful for cohesion, trust, fraud-ring, and dense-neighborhood analysis.

Consumer-Level Explanation

Use clustering_coefficient when measure local triadic closure around nodes 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. These procedures explain local structure rather than global communities. High triangle counts can mean cohesive groups, repeated event modeling, or simply high degree, so interpret them with the graph model in mind.

Conceptual Explanation

clustering_coefficient 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

This differs from community detection because it is a local closure metric, not a full partitioning algorithm

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

This differs from community detection because it is a local closure metric, not a full partitioning algorithm.

Advanced Example

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

CALL nexyron.clustering_coefficient() YIELD node_id, coefficient, triangle_count
RETURN node_id, coefficient, triangle_count
LIMIT 10

Real Use Cases

Real Limitations And Tradeoffs