About PageRank Analysis
PageRank is a network statistic for directed graphs. It estimates how important each page is inside a link system. A page receives value from pages that point to it. A vote from a strong page usually matters more than a vote from a weak page. This idea makes PageRank useful for websites, citations, recommendations, and knowledge maps.
What This Tool Measures
This calculator turns your links into a transition model. Each step moves rank through outgoing links. The damping factor adds a random jump. That jump prevents trapped loops from controlling the whole model. It also gives every page a fair starting chance. Dangling pages need special care. They have no outgoing links. The calculator can spread their rank across all pages, using the selected personalization weights.
Why Iterations Matter
PageRank is not usually solved in one direct step. The score vector is updated again and again. Each update compares the new scores with the old scores. When the difference becomes smaller than your tolerance, the process stops. A lower tolerance gives a tighter result. It may also require more iterations.
Statistical Interpretation
The final rank can be read as long run probability. Imagine a visitor moving through links forever. Sometimes the visitor jumps to another page. The PageRank value estimates the share of time spent on each page. The scores should normally sum to one after normalization. Higher values suggest stronger centrality, not guaranteed quality.
Good Input Practice
Use clear page labels. Add one directed edge per line. Write source first, then target. Remove duplicate links unless they represent repeated weighted behavior. Compare several damping values when the graph is small. A common value is 0.85, but no single value fits every study.
Using Results
The ranked table shows score, percent share, inbound count, and outbound count. Inbound count helps explain raw attention. Outbound count shows how widely each page distributes rank. Download the table for reports or audits. Use the PDF for quick sharing. Use CSV when you need spreadsheet review or further modeling. Review convergence status before trusting outputs. If convergence fails, raise iteration limits. You can also relax tolerance. Unstable graphs often need better link cleanup and clearer page definitions during testing.