fm-graph-cluster

Segmenting a FileMaker solution involves clustering its object graph into logical modules using community detection algorithms. This process identifies business domains, central hubs, and cross-file dependencies to simplify architectural navigation.

Graph clustering for FileMaker architecture analysis uses Louvain or Leiden algorithms to detect community structures within the object graph. It automatically assigns semantic names to identified modules and generates reports on module distribution.

Yes, analyzing FileMaker object graphs requires DuckDB to store the catalog data, Node.js, and Python with the igraph library. These dependencies are necessary to perform the multi-candidate resolution sweeps and semantic naming.

Yes, you can identify business domains and central hubs by applying community detection algorithms to the FileMaker object graph. The analysis generates comprehensive reports detailing module distribution and cross-file dependencies.

The best way to visualize cross-file dependencies is by extracting the FileMaker object graph into DuckDB and applying community detection algorithms. This approach reveals the underlying modular structure and highlights critical dependency hubs.

Semantic naming is needed during graph clustering to automatically assign human-readable names and descriptions to identified modules based on their content and relationships. This makes the segmented architecture easier for developers and AI agents to understand.