Graph Algorithms Laravel Package

Product Decisions This Supports

• Build vs. buy, with a real threshold. This package replaces a multi-week algorithm build — PageRank, degree centrality, Dijkstra, A*, BFS/DFS, Tarjan SCC, topological sort with cycle detection, MST — with a composer require plus a product layer. The honest buy case is two or more algorithm families in the next two quarters. A single BFS over a small graph is ~40 lines and not worth a dependency, a transitive graph-core requirement, and a bus-factor.
• Roadmap sequencing, algorithm by algorithm. Each capability maps to a distinct shippable feature, so the package can fund itself incrementally rather than as one big bet:
  • PageRank / degree centrality → influence, "top contributor," "most-cited," "key connector" ranking surfaces.
  • Topological sort with cycle detection → dependency and ordering UX (build pipelines, prerequisite chains, workflow validation with circular-dependency warnings).
  • Tarjan SCC → clustering and grouping features, plus circularity detection (mutually reinforcing citation rings, duplicate clusters).
  • Dijkstra / A* → any "cheapest / fastest route" experience over weighted edges.
  • BFS/DFS → reachability, connectivity, "related items" traversal.
• Feature validation before platform investment. Typed contracts (CentralityAlgorithmInterface, PathfindingAlgorithmInterface, TraversalAlgorithmInterface) and immutable results (PathResult, MstResult) make a spike cheap: "does PageRank on our data produce rankings users actually act on?" Cheap evidence, reversible call. If the answer is no, we've spent a sprint, not a quarter.
• A real architectural commitment, not just a dependency. Adoption means standardizing on nexus-scholar/graph-core's model (string node IDs, array edge attributes, GraphInterface) and the integer-indexed AlgorithmGraph proxy used during execution. That is a data-model decision with switching costs, and it belongs in the PM's risk register alongside engineering's.
• Explicit product semantics for edge cases — a UX decision you get for free. The package's error contract dictates user-facing behavior, so confirm it matches what product wants: Dijkstra rejects negative weights, topological sort reports cycles, MST returns no result for disconnected graphs, pathfinding returns null when no path exists. Each of those is a screen state you must design and copy for.
• Effort estimation you can trust. "PHP 8.2+, graph-core ^1.0, composer require, five named classes" is a far cheaper estimate to defend than "build graph algorithms." The reverse is also true: composer test, composer stan, and composer bench shipping in the README tells you the maintenance surface you'd inherit if you fork.
• Deprioritize signals. Small graphs (sub-100 nodes), capability already in-house, a non-PHP stack, or a hard PHP < 8.2 constraint all push toward skipping — and revisiting when a second algorithm need appears.

The scored opportunity (8.24) against 1 star / 0 dependents is the signal to act on: high capability value, near-zero adoption. That gap is whitespace for a scoped pilot, not a mandate for core infrastructure.

When to Consider This Package

Consider it when:

• The product needs non-trivial graph computation — centrality, SCCs, topological ordering, weighted shortest paths — not just adjacency listing.
• The stack is PHP 8.2+ / Laravel. Note the package is deliberately framework-agnostic: there is no service provider, facade, or config file, so Laravel integration is a small amount of work you own (bind the interfaces in a provider, inject the algorithm classes).
• You'd otherwise hand-roll PageRank or Tarjan's algorithm without an in-house graph specialist — the classic source of subtle, expensive, hard-to-reproduce bugs.
• Graph work is batch or request-scoped analytics (dashboards, reports, ranking jobs), not sub-millisecond hot-path serving.
• You want typed, mockable contracts you can stub in tests, plus immutable result objects you can persist, queue, or render directly.

Look elsewhere — or build — when:

• Maturity risk is unacceptable. 1 star, 0 dependents, a single maintainer, and a recent rename: supply-chain and bus-factor exposure. On revenue-critical paths, treat it as "read the source, plan a fork," not "set and forget."
• You need performance guarantees. No published benchmarks back the README's "high-performance" claim. Validate the AlgorithmGraph conversion at your real sizes (10k–100k nodes) before committing. composer bench exists — use it on your data, not the fixtures.
• You need I/O, persistence, or visualization. The package computes; it does not store, serialize, or render. Those are your problems (graph-core helps only partially with export primitives).
• Your graph model doesn't fit. Custom node types or non-array edge attributes need adapters — real work that erodes the buy case.
• Scope is a single trivial need, the stack is non-PHP, or an existing graph dependency would conflict.

Adoption gate I'd apply: a proof-of-concept against production-shaped data, a documented fork/vendor contingency, deliberate version pinning across the v1.2.0 Composer rename (nexus-scholar/graph-algorithms, with a replace bridge for mbsoft31/graph-algorithms; PHP namespaces stay Mbsoft\Graph\Algorithms), and a review of the transitive graph-core dependency — all before it touches a critical path.

How to Pitch It (Stakeholders)

For executives: "This is a small, MIT-licensed PHP library that gives us production-ready graph algorithms — influence ranking, shortest paths, dependency ordering, cluster detection — that we would otherwise build from scratch over months. It lets us test graph-powered features in weeks and invest further only if the data supports it. The honest caveat: it's early-stage, single-maintainer, and derived from a research product, so I'm proposing we adopt it for one pilot feature with a fallback plan, not as core infrastructure on day one. The experiment is cheap; the information value is high."

For engineering: "Standard algorithms — PageRank, degree centrality, Dijkstra, A*, BFS/DFS, Tarjan SCC, topological sort with cycle detection, MST — behind typed interfaces and immutable results (PathResult, MstResult), with custom weight and heuristic callbacks for non-default edge semantics. It sits on graph-core, so it slots in next to existing graph storage; it's framework-agnostic, so we wire our own bindings. Watch the v1.2.0 Composer rename (nexus-scholar/graph-algorithms with a replace bridge, namespaces still Mbsoft\Graph\Algorithms) — pin versions and migrate the require line deliberately. Two asks before we commit: benchmark AlgorithmGraph at our real graph sizes, and confirm we're comfortable owning a fork if upstream goes quiet. The codebase is readable in an afternoon — that's the real mitigation."

For product managers: "Think of this as a capability unlock with scoped risk. It enables a class of features we currently can't ship without specialist effort — 'who are the most influential nodes in this network,' 'what order should these steps run in,' 'is there a hidden circular dependency.' The play is to pick one such feature, ship it against real data, and measure whether users act on the output. If they do, we've validated the graph roadmap cheaply; if not, we've spent a sprint. Either way we learn something the roadmap currently assumes."