Records how the PageRank stationary vector was obtained: which solver ran, how many iterations it used (when that is observable), and how well the returned vector satisfies the PageRank fixed-point equation. It is attached to the result of [compute_pagerank()] / [pagerank()] as the `"convergence"` attribute and is the companion to the [transition_audit][transition_audit] provenance record.
Details
## Why the solver matters
`igraph::page_rank()` offers two back-ends:
- `"prpack"`
(default) A direct/sparse solver (the PRPACK library). It is fast and exact to machine precision, but it is **not** iterative in any way it exposes: there is no iteration count and no tolerance knob, so `iters` is reported as `NA` and `eps` / `niter` have no effect.
- `"arpack"`
An iterative eigensolver. It honors a tolerance and a maximum iteration count and reports the iterations it actually used and whether it converged. This is the only back-end on which `eps` and `niter` take effect; supplying either to [compute_pagerank()] / [pagerank()] therefore transparently selects it.
The old `igraph` `eps` / `niter` arguments to `page_rank()` were removed in modern `igraph` (2.x); this package re-introduces them as friendly aliases that map onto the ARPACK `options$tol` / `options$maxiter` controls.
## The residual is solver-independent
Regardless of back-end, `residual` is computed here directly from the returned vector as the L1 norm of one PageRank operator application, \(\|G x - x\|_1\), where \(G\) is the Google operator implied by the scored graph, the damping factor, and the teleport vector (uniform, or the supplied TIPR prior). This is the standard Kamvar, Haveliwala & Golub (2004) stopping criterion, evaluated *post hoc* so it is a genuine, comparable quality check across both solvers (a converged solution sits near machine precision). `tol_met` reports whether `residual` is at or below `tol` (the supplied `eps`, or the conventional default of `1e-3` when `eps` is `NULL`), additionally requiring `info == 0` for the ARPACK back-end.
## Iteration-count rule of thumb
Power-iteration PageRank needs about \(\log_{10}(\tau) / \log_{10}(\alpha)\) iterations to reach residual \(\tau\) at damping \(\alpha\) (Langville & Meyer, 2004). At \(\tau = 10^{-8}\): \(\alpha = 0.85\) needs ~114, \(\alpha = 0.95\) ~362, and \(\alpha = 0.99\) ~1,833 iterations — so a high damping factor degrades convergence sharply. ARPACK is not plain power iteration, so its reported `iters` is typically far lower, but the same qualitative warning applies: raise `niter` if you raise the damping factor toward 1.