Graph Algorithms

Trigger Boundary

• •Use when algorithmic correctness or complexity drives implementation risk.
• •Do not use for persistence-schema decisions; use db-*.
• •Do not use for runtime deployment topology; use deployment-* or kubernetes-*.

Goal

Deliver correct and efficient computational designs with clear tradeoffs.

Inputs

• •Change scope and risk profile
• •Domain evidence for graph modeling and traversal/path algorithm selection
• •Operational, compliance, and rollout constraints

Outputs

• •Graph algorithm choice rationale
• •Decision log for graph modeling and traversal/path algorithm selection
• •Verification checklist with measurable pass-fail criteria

Workflow

1. •Clarify outcomes and hard constraints for graph modeling and traversal/path algorithm selection.
2. •Produce options and select an approach for graph modeling and traversal/path algorithm selection.
3. •Evaluate trade-offs across security, performance, operability, and maintainability.
4. •Verify decisions using graph property tests and path correctness validation.
5. •Publish decisions, residual risks, and accountable follow-up actions.

Quality Gates

• •Scope and assumptions for graph modeling and traversal/path algorithm selection are explicit and reviewable.
• •Decision rationale is backed by evidence instead of preference.
• •Rollout and rollback criteria are defined when production impact exists.
• •Residual risks have owners, due dates, and verification steps.

Failure Handling

• •Stop when graph representation or algorithm choice mismatches problem constraints.
• •Escalate when accepted risk exceeds team policy thresholds.