strongly_connected_components
Generic Description
Partition a directed graph into strongly connected groups.
Simple example:
CALL nexyron.strongly_connected_components()
Component and ordering procedures describe reachability structure: which nodes belong to the same connected region, which directed regions are mutually reachable, or which order respects dependency edges.
Consumer-Level Explanation
Use strongly_connected_components when partition a directed graph into strongly connected groups 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. Use these procedures to understand graph feasibility and dependency shape before running heavier algorithms or interpreting isolated metric scores.
Conceptual Explanation
strongly_connected_components 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 weak connectivity by requiring mutual reachability under direction
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 weak connectivity by requiring mutual reachability under direction.
Advanced Example
This example yields named columns from strongly_connected_components and returns a bounded result shape that downstream planner tooling can compose without guessing column names or row grain.
CALL nexyron.strongly_connected_components() YIELD node_id, component_id
RETURN node_id, component_id
LIMIT 10
Real Use Cases
- feedback loop detection
- cyclic subsystem discovery
- workflow graph analysis
- compare algorithm output with domain labels or time windows before operationalizing it
- feed graph-analytic scores into ranked reports, feature contracts, or investigation queues only after checking the modeling assumptions
Real Limitations And Tradeoffs
- only meaningful when edge direction is semantically important
- algorithm output reflects the projected graph, not an abstract real-world network outside the data model
- nearby algorithms can answer different questions even when their output columns look similar