Pagerank — where it appears
Named by 4 essays across one field — each of them below, with the objects they name alongside it.
A ranking that is an eigenvector
PageRank is the stationary vector of a walk that follows links with probability α and jumps at random otherwise. The iteration and the elimination agree to 4·10⁻¹⁷. What α is set to changes which pages come third, fourth and fifth.
The rate is the second eigenvalue
A walk forgets where it started at a rate the graph's second eigenvalue names exactly. Across three orders of magnitude in the step count the prediction is five per cent high — and the published rate for PageRank is right for a reason nobody states, which is that a link graph is in pieces.
A chain with no stationary vector
A page with no outgoing links loses forty per cent of the walker's probability in six hundred steps. A directed cycle never converges at all. And on a graph whose links only run one way, the entire rank of half the vertices is exactly one minus the teleportation parameter.
A ranking whose order is not determined
Of the fourteen adjacent comparisons in a top-fifteen, fourteen survive perturbing the arithmetic at the rounding level, eleven survive moving the teleportation parameter across its usual range, and three survive removing one link. The computation is the strongest part of the answer.
Named alongside it
The objects these essays reach for when they reach for this one.
Stationary distributionTeleportationDangling nodeMixing timeParameter choicePerron frobeniusPower iterationRandom walkRegularisationBackward errorBipartiteCentrality