  PageRank is a popular centrality metric that assigns importance to the
vertices of a graph based on its neighbors and their score. Efficient parallel
algorithms for updating PageRank on dynamic graphs is crucial for various
applications, especially as dataset sizes have reached substantial scales. This
technical report presents our Dynamic Frontier approach. Given a batch update
of edge deletion and insertions, it progressively identifies affected vertices
that are likely to change their ranks with minimal overhead. On a server
equipped with a 64-core AMD EPYC-7742 processor, our Dynamic Frontier PageRank
outperforms Static, Naive-dynamic, and Dynamic Traversal PageRank by 7.8x,
2.9x, and 3.9x respectively - on uniformly random batch updates of size 10^-7
|E| to 10^-3 |E|. In addition, our approach improves performance at an average
rate of 1.8x for every doubling of threads.
