  PageRank is a widely used centrality measure that assesses the significance
of vertices in a graph by considering their connections and the importance of
those connections. Efficiently updating PageRank on dynamic graphs is essential
for various applications due to the increasing scale of datasets. This
technical report introduces our improved Dynamic Frontier (DF) and Dynamic
Frontier with Pruning (DF-P) approaches. Given a batch update comprising edge
insertions and deletions, these approaches iteratively identify vertices likely
to change their ranks with minimal overhead. On a server featuring a 64-core
AMD EPYC-7742 processor, our approaches outperform Static and Dynamic Traversal
PageRank by 5.2x/15.2x and 1.3x/3.5x respectively - on real-world dynamic
graphs, and by 7.2x/9.6x and 4.0x/5.6x on large static graphs with random batch
updates. Furthermore, our approaches improve performance at a rate of 1.8x/1.7x
for every doubling of threads.
