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.

翻译：PageRank是一种流行的中心性度量方法，它根据图中顶点的邻居及其得分来赋予顶点重要性。针对动态图上的PageRank更新开发高效并行算法，对于各类应用至关重要，尤其是在数据集规模已达到相当量级的情况下。本技术报告提出了我们的动态前沿方法。给定一批边删除和插入的更新操作，该方法能够渐进地识别出可能以最小开销改变其排名的受影响顶点。在配备64核AMD EPYC-7742处理器的服务器上，我们的动态前沿PageRank在均匀随机批更新（规模从10^-7 |E|到10^-3 |E|）中，分别比静态PageRank、朴素动态PageRank和动态遍历PageRank快7.8倍、2.9倍和3.9倍。此外，我们的方法在线程数每翻倍时，平均性能提升1.8倍。