Module incremental_pagerank 

Incremental PageRank over a streaming DynamicGraph.

A single edge insertion or deletion perturbs only a handful of rows of the Google matrix G = d · (A D^{-1} + dangling teleport) + (1-d)/n · 1 1^T, so the previous stationary distribution is an excellent warm start for the new one. IncrementalPageRank keeps the current rank vector and, after each batch of apply_* mutations, resumes the power iteration from that vector rather than from the uniform cold start. The fixed point of the power iteration is unique (the Google matrix is column-stochastic, irreducible and aperiodic for 0 < d < 1), so warm-starting converges to exactly the same ranks an independent cold-start solve would — only faster.

Dangling vertices (out-degree zero) redistribute their mass uniformly, and the result is renormalised to sum to one, matching crate::centrality::pagerank::pagerank.

Structs

IncrementalPageRank

Stateful incremental PageRank engine bound to a mutable graph.