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.

翻译：PageRank是一种广泛使用的重要性度量指标，通过考虑图中顶点的连接关系及其连接的重要性来评估顶点的重要性。随着数据集规模的不断扩大，高效更新动态图上的PageRank对于各类应用至关重要。本技术报告介绍了我们改进的动态前沿（DF）和带剪枝的动态前沿（DF-P）方法。给定包含边插入和删除操作的批量更新时，这些方法能以最小开销迭代识别可能改变排名的顶点。在配备64核AMD EPYC-7742处理器的服务器上，我们的方法在真实动态图上比静态和动态遍历PageRank分别快5.2倍/15.2倍和1.3倍/3.5倍；在随机批量更新的大型静态图上则分别快7.2倍/9.6倍和4.0倍/5.6倍。此外，每当线程数翻倍时，我们的方法性能提升速率达1.8倍/1.7倍。