Personalized PageRank (PPR) is a seed-specific generalization of PageRank. The random surfer follows an outgoing edge with probability $1-\alpha$ and teleports with probability $\alpha$, but the uniform teleport distribution is replaced by a user-specified preference vector $\mathbf{r}$ over the nodes. The resulting stationary score $\boldsymbol{\pi}_{\mathbf{r}}$ concentrates near the seeds in $\mathbf{r}$ rather than spreading uniformly across the graph. Formally, $\boldsymbol{\pi}_{\mathbf{r}}$ is the unique solution of $\boldsymbol{\pi}_{\mathbf{r}} = (1-\alpha), P^{\top} \boldsymbol{\pi}_{\mathbf{r}} + \alpha, \mathbf{r},$ where $P$ is the row-stochastic transition matrix of the graph. Jeh and Widom (2003) introduced a hub decomposition scheme that allows many personalized vectors to be computed efficiently at web scale. A truncated variant restricts the computation to the highest-weight entries or bounds the iteration depth, trading recall for compute and yielding the truncated personalized PageRank commonly used as a graph-retrieval baseline.