Internal Link Equity Grapher & Orphan Page Radar
Visualize internal PageRank distribution and spot orphan pages instantly from your crawl data with zero external dependencies.
Problem it addresses
Create a standalone Python CLI tool that parses standard crawl export CSVs (Screaming Frog or Sitebulb format), builds a directed graph of internal links, computes internal PageRank using iterative power-iteration, identifies orphan pages (0 inlinks), and generates an interactive, standalone HTML topology visualization report.
Who it is for
PRO operating in high-growth e-commerce, digital publishing, and direct-response marketing across Saudi Arabia, UAE, and Egypt.
What is inside
Key files listed in the README repository architecture:
LICENSEREADME.mdreports/sample_report.htmlsample_data/crawl_export.csvsrc/pagerank_graph.pytests/test_graph.py
How to evaluate and use it
- Read the README first and confirm that the documented problem matches your use case.
- Inspect the listed files to find the data, tools, templates, and tests relevant to your task.
- Follow the repository’s Quick Start and check prerequisites before running commands.
- Compare outputs with documented examples and review limitations before using them in a decision or client workflow.
Quick Start from the README: python -m unittest discover -s tests · python src/pagerank_graph.py --help
Scope and limitations
- Graph Traversal Scale: Large enterprise e-commerce catalogs (>50,000 URLs) require directed graph adjacency matrices to compute PageRank without memory exhaustion.
- Canonical & Noindex Filters: Orphan page radar must distinguish between intentionally hidden landing pages and accidental orphans missing internal links.
- Crawl Depth Limits: Search bots prioritize pages within 3 to 4 click depths from the root domain.
This page summarizes the available documentation; it does not establish performance or business results. Verify data and prerequisites before use.