min_cost_max_flow
Generic Description
Maximize flow while minimizing total cost.
Simple example:
CALL nexyron.min_cost_max_flow({source: 0, sink: 1})
Tree and flow procedures optimize connectivity or movement through weighted graphs. They are useful for network design, allocation, dependency coverage, and capacity analysis.
Consumer-Level Explanation
Use min_cost_max_flow when maximize flow while minimizing total cost 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 algorithms make stronger assumptions about weights, terminals, prizes, and capacities than simple traversal, so parameter meaning must be explicit.
Conceptual Explanation
min_cost_max_flow 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 max_flow by caring not only about quantity moved but also the price of moving it
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 max_flow by caring not only about quantity moved but also the price of moving it.
Advanced Example
This example yields named columns from min_cost_max_flow and returns a bounded result shape that downstream planner tooling can compose without guessing column names or row grain.
CALL nexyron.min_cost_max_flow({source: 0, sink: 1}) YIELD source, target, flow, cost, max_flow
RETURN source, target, flow, cost, max_flow
LIMIT 10
Real Use Cases
- capacity routing with cost tradeoffs
- supply or logistics modeling
- network optimization
- 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
- needs both capacity and cost semantics to be trustworthy
- 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