Graph Core Laravel Package

Product Decisions This Supports

• Feature Development: Enables building citation networks, knowledge graphs, and dependency graphs without reinventing graph algorithms from scratch. Ideal for academic, research, or data-driven applications requiring graph traversal, visualization, or analysis.
• Roadmap Alignment: Supports a modular architecture where graph-core handles foundational graph structures, while graph-algorithms (or future packages) can extend functionality (e.g., pathfinding, centrality metrics). Reduces technical debt by decoupling core logic from domain-specific implementations.
• Build vs. Buy: Buy for teams needing a production-ready, lightweight graph library in PHP. Avoids dependency bloat (no external libraries except ext-dom for exports) and provides type safety (PHP 8.2+). Justify adoption if:
  • Your use case aligns with directed/undirected graphs, node/edge attributes, or visualization exports (Cytoscape.js, GraphML, GEXF).
  • You’re building scholarly tools, network analysis, or workflow automation where graph structures are central.
• Use Cases:
  • Academic Research: Citation networks, co-authorship graphs, or bibliometric analysis.
  • Data Visualization: Export graphs to tools like Gephi, Cytoscape.js, or yEd for interactive exploration.
  • Dependency Management: Model build systems, package dependencies, or state machines.
  • Recommendation Engines: Collaborative filtering or content-based recommendations using graph traversal.

When to Consider This Package

• Adopt if:

  • You need a PHP-native graph library with no external dependencies (except ext-dom for XML exports).
  • Your use case requires node/edge attributes, subgraph views, or efficient adjacency lookups (integer indexing).
  • You’re building visualization-heavy applications and need exports to Cytoscape.js, GraphML, or GEXF.
  • You want PHP 8.2+ type safety and a lightweight (~18KB) package.
  • You’re part of the Nexus Scholar ecosystem or working on scholarly/citation networks.

• Look elsewhere if:

  • You need advanced algorithms (e.g., shortest paths, community detection) → Pair with nexus-scholar/graph-algorithms or use NetworkX (Python).
  • You require persistent storage (e.g., databases) → Consider Neo4j, ArangoDB, or Elasticsearch.
  • Your graph is extremely large (millions of nodes) → Evaluate scalability (this package is optimized for in-memory use).
  • You need weighted graphs with custom traversal logic → Extend the package or use a more feature-rich library like Jung (Java) or igraph (R).
  • You’re not using PHP 8.2+ → This package drops support for older versions.
  • You need real-time collaboration → This is a local/offline graph structure.

How to Pitch It (Stakeholders)

For Executives:

"This is a strategic, low-risk investment in a lightweight PHP graph library that enables us to build scalable network analysis tools without heavy dependencies. It’s MIT-licensed, actively maintained (last release: May 2026), and aligns with our roadmap for scholarly data visualization and citation networks. By adopting this, we avoid reinventing graph algorithms, reduce technical debt, and can quickly prototype features like co-authorship maps or dependency graphs—all while supporting exports to industry-standard tools like Gephi and Cytoscape.js. The package is dependency-free (except for XML exports), so it won’t bloat our stack, and its integer-indexed adjacency model ensures performance even with large graphs."

For Engineering:

*"graph-core is a batteries-included but extensible graph library for PHP 8.2+. Here’s why it’s a win:

• No external deps (except ext-dom for XML exports), so it’s fast and predictable.
• Supports directed/undirected graphs with node/edge attributes, which fits our use cases for citation networks and knowledge graphs.
• Subgraph views let us filter graphs without copying data, saving memory.
• Exporters for Cytoscape.js, GraphML, and GEXF mean we can visualize graphs in tools like Gephi with minimal effort.
• Type-safe (PHP 8.2+) and well-tested (Pest suite, CI coverage).
• Pairs with graph-algorithms for advanced features like centrality metrics or pathfinding if needed.

Trade-offs:

• Not for distributed graphs or persistent storage (use Neo4j/ArangoDB instead).
• If we need real-time updates, we’ll need to layer caching or a DB on top.

Next steps:

1. Spike: Benchmark against rolling our own graph structure (likely faster to integrate this).
2. Align with graph-algorithms: If we need traversal/pathfinding, we can add that package as a dependency.
3. Document exports: Ensure our visualization team knows how to use Cytoscape.js/GraphML outputs."*

Key Ask: "Let’s start with a proof-of-concept for our citation network feature—this package will save us 3–6 months of dev time."