via MarkTechPost
Using Graphify and NetworkX to Map Python Codebase Structure with God Nodes, Communities, and Architecture Visualizations
In this tutorial, we build a fully offline workflow using Graphify to transform a realistic multi-module Python application into a knowledge graph for deeper architectural insights. By 2026, as codebases grow increasingly complex—often spanning microservices, AI pipelines, and distributed systems—automated structural mapping has become essential for maintainability and refactoring. This approach empowers developers to visualize dependencies, identify critical components, and detect architectural anti-patterns without relying on external APIs or cloud services.
We start by installing Graphify and supporting graph libraries, then generate a small but connected sample application with configuration, database, authentication, service, API, cache, model, and SQL layers. The graph is extracted locally using Graphify’s tree-sitter-based analysis, ensuring no API key or LLM backend is required. After loading the generated `graph.json` into NetworkX, we analyze the codebase structure through file types, relationship types, centrality scores, community detection, and shortest paths among important symbols. We also create both static and interactive visualizations, making it easier to understand how modules, classes, functions, and database objects connect across the architecture.
This method is particularly valuable for teams adopting 2026’s best practices in code observability, where automated knowledge graphs complement traditional documentation and static analysis. By mapping god nodes (highly connected components) and communities, engineers can quickly spot overburdened modules, plan modularization, and assess the impact of changes—all while keeping sensitive code fully offline and secure. The full code and walkthrough are available in the accompanying Jupyter notebook.