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Personalized PageRank (PPR) is a critical measure of the importance of a nodet to a source node s in a graph. The Single-Source PPR (SSPPR) query computesthe PPR's of all the nodes with respect to s on a directed graph $G$ with $n$nodes and $m$ edges, and it is an essential operation widely used in graphapplications. In this paper, we propose novel algorithms for solving twovariants of SSPPR: (i) high-precision queries and (ii) approximate queries. Forhigh-precision queries, Power Iteration (PowItr) and Forward Push (FwdPush) aretwo fundamental approaches. Given an absolute error threshold $\lambda$, theonly known bound of FwdPush is $O(\frac{m}{\lambda})$, much worse than the $O(m\log \frac{1}{\lambda})$-bound of PowItr. Whether FwdPush can achieve the samerunning time bound as PowItr does still remains an open question in theresearch community. We give a positive answer to this question by showing thatthe running time of a common implementation of FwdPush is actually bounded by$O(m \cdot \log \frac{1}{\lambda})$.Based on this finding, we propose a newalgorithm, called Power Iteration with Forward Push (PowerPush), whichincorporates the strengths of both PowItr and FwdPush. For approximate queries(with a relative error $\epsilon$), we propose a new algorithm, calledSpeedPPR, with overall expected time bounded by $O(n \cdot \log n \cdot \log\frac{1}{\epsilon})$ on scale-free graphs. This bound greatly improves the$O(\frac{n \cdot \log n}{\epsilon})$ bound of a state-of-the-art algorithmFORA.

Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push