Do I need to install anything?

No. It runs in any modern browser, free, with no account required.

For Educators · PageRank

PageRank for a dependency graph

Built for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs. Simulate a dependency graph live below — adjust the inputs and watch it respond, right in your browser.

PageRankLive

the algorithm that built Google

Controls

Presets

Damping factor d

PageRank ranks nodes by importance: a page is important if important pages link to it. It is computed by imagining a random surfer who follows links with probability d and jumps randomly otherwise, then finding where they spend the most time. This eigenvector of the link matrix launched Google and now ranks everything from proteins to social influence.

▶ Run in Python

Data Inspector

Damping0.85

Top node23.6%

Methodpower iteration

Governing equation

P R (p) = \frac{1 - 0.85}{6} + 0.85 q \sum \frac{P R ( q )}{L ( q )}

Reading this result: PageRank is the stationary distribution of a random surfer who follows links with probability d and teleports to a random page with probability 1−d. At d=0.85 link structure dominates but teleport still keeps every node reachable.

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Frequently asked questions

Is this good for educators?: Yes — this version of "PageRank for a dependency graph" is framed for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.