prize_steiner_tree
Generic Description
Steiner-style optimization with node prizes and edge costs.
Simple example:
CALL nexyron.prize_steiner_tree({prize: 'prize', root: 0})
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 prize_steiner_tree when steiner-style optimization with node prizes and edge costs 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
prize_steiner_tree 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 plain Steiner by trading off inclusion value against connection cost
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 plain Steiner by trading off inclusion value against connection cost.
Advanced Example
This example yields named columns from prize_steiner_tree and returns a bounded result shape that downstream planner tooling can compose without guessing column names or row grain.
CALL nexyron.prize_steiner_tree({prize: 'prize', root: 0}) YIELD source, target, weight, total_weight, total_prize, objective
RETURN source, target, weight, total_weight, total_prize, objective
LIMIT 10
Real Use Cases
- investigation graph extraction where some nodes are more valuable
- budget-aware subgraph selection
- signal-vs-cost 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
- objective tuning is non-trivial and domain-sensitive
- 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