{\em Personalized PageRank (PPR)} stands as a fundamental proximity measure
in graph mining. Since computing an exact SSPPR query answer is prohibitive,
most existing solutions turn to approximate queries with guarantees. The
state-of-the-art solutions for approximate SSPPR queries are index-based and
mainly focus on static graphs, while real-world graphs are usually dynamically
changing. However, existing index-update schemes can not achieve a sub-linear
update time. Motivated by this, we present an efficient indexing scheme to
maintain indexed random walks in expected O(1) time after each graph update.
To reduce the space consumption, we further propose a new sampling scheme to
remove the auxiliary data structure for vertices while still supporting O(1)
index update cost on evolving graphs. Extensive experiments show that our
update scheme achieves orders of magnitude speed-up on update performance over
existing index-based dynamic schemes without sacrificing the query efficiency