  Growing popularity of social networks demands a highly efficient Personalized
PageRank (PPR) updating due to the fast-evolving web graphs of enormous size.
While current researches are focusing on PPR updating under link structure
modification, efficiently updating PPR when node insertion/ deletion involved
remains a challenge. In the previous work called Virtual Web (VW), a few VW
architectures are designed, which results in some highly effective
initializations to significantly accelerate PageRank updating under both link
modification and page insertion/deletion. In the paper, under the general
scenario of link modification and node insertion/deletion we tackle the fast
PPR updating problem. Specifically, we combine VW with the TrackingPPR method
to generate initials, which are then used by the Gauss-Southwell method for
fast PPR updating. The algorithm is named VWPPR method. In extensive
experiments, three real-world datasets are used that contain 1~5.6M nodes and
6.7M~129M links, while a node perturbation of 40k and link perturbation of 1%
are applied. Comparing to the more recent LazyForwardUpdate method, which
handles the general PPR updating problem, the VWPPR method is 3~6 times faster
in terms of running time, or 4.4~10 times faster in terms of iteration numbers.
