PageRank-style propagation is a family of graph scoring rules in which node importance is defined as the stationary distribution of a random walk on the graph that, at each step, either follows an outgoing edge with probability $1-\alpha$ or teleports to a seed distribution with probability $\alpha$ (the damping/restart term). The original PageRank (Brin & Page, 1998) uses a uniform teleport over all nodes, while personalized PageRank (Jeh & Widom, 2003) and topic-sensitive PageRank (Haveliwala, 2002) replace the teleport with a seed-specific distribution, so the stationary score concentrates near the seeds. Local/approximate personalized PageRank (Andersen, Chung & Lang, 2006) computes the same seed-localized stationary scores efficiently via push-style updates without touching the whole graph. The common structural feature across all variants is that a node's score is an aggregate over infinitely many walks weighted by damping, rather than a function of any single shortest or best path.