Using Graphify and NetworkX to Map Python Codebase Structure with God Nodes, Communities, and Architecture Visualizations

In this tutorial , we build a fully offline Graphify workflow that turns a realistic multi-module Python application into a knowledge graph. 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. We extract the graph locally using Graphify’s tree-sitter-based analysis, so we do not need an API key or any LLM backend. After loading the generated graph.json into NetworkX, we analyze the codebase’s structure using file types, relationship types, centrality scores, community detection, and shortest paths among important symbols. Also, we create both static and interactive visualizations, making it easier to understand how modules, classes, functions, and database objects connect across the project.

We install Graphify along with the graph analysis and visualization libraries needed for the tutorial. We import the required Python modules, including NetworkX for graph processing and Matplotlib for static plotting. We also suppress unnecessary warnings so the notebook output stays clean and focused.

We create a realistic sample application with multiple Python modules and one SQL schema file. We design the files to include meaningful cross-module relationships, such as imports, function calls, service dependencies, authentication logic, database access, and rate limiting. We then write all these files to a local sample_app directory, giving Graphify a complete mini-codebase to analyze.

We run Graphify locally on the generated application and extract the project knowledge graph without using any API key or LLM backend. We locate the generated graph.json file and load it into NetworkX using a version-proof node-link loader. We then convert the graph into an undirected form for easier structural analysis and define a helper function to display readable node labels.

We analyze the extracted graph by summarizing node types, edge relationships, and confidence levels. We compute degree centrality and betweenness centrality to identify important “god nodes” that connect many parts of the application. We also detect communities in the graph and trace a shortest path between key components to understand how parts of the codebase are connected.

We visualize the knowledge graph using both static and interactive methods. We first create a Matplotlib graph where node size represents centrality and node color represents community membership. We then build an interactive Pyvis visualization and run Graphify’s CLI commands to query the graph, find paths, and explain selected symbols.

In conclusion, we have a complete local pipeline for converting source code into a useful knowledge graph and studying it with graph analytics. We saw how Graphify extracts meaningful relationships from a Python and SQL codebase, and we use NetworkX to identify central “god nodes,” detect communities, and trace paths between components such as authentication and database logic. We also generated visual outputs that help us inspect the architecture from both a high-level and interactive perspective. This workflow gives us a pathway to reason about code structure, dependency flow, architectural hotspots, and cross-file connections without relying on external APIs, making it useful for codebase exploration, documentation, refactoring, and software architecture analysis.