Calculate Single-node PageRank

Single-node PageRank is a method that focuses on calculating the PageRank score of a specific web page on the Internet. This method pays special attention to the calculation of PageRank scores for a small number of specific web pages, such as calculating the PageRank scores of several well-known websites. It is a variant of the PageRank algorithm that focuses on calculating the PageRank value of a single node in the network. The PageRank algorithm was proposed by Google founders Larry Page and Sergey Brin in 1998 to measure the importance or quality of web pages. Single-node PageRank calculation is developed on this basis, aiming to estimate the probability of a random walk terminating at a specific node.

Single-point PageRank calculation determines the importance of nodes through a random walk model. In the PageRank algorithm, the PageRank value of a node is transferred from the PageRank values of all links pointing to the node, while taking into account the damping factor to simulate the behavior of random browsers occasionally jumping to any web page. Single-point PageRank calculation focuses on calculating the probability that a random walk starting from all nodes will eventually reach a specific target node.

Researchers from Renmin University of China published a paper at the 2024 ACM Annual Conference on Theory of Computing (STOC) titled "Revisiting Local Computation of PageRank: Simple and Optimal", optimizing the computational complexity of single-point PageRank to the theoretical optimum. This study successfully optimized the computational complexity of single-point PageRank by reanalyzing the BiPPR algorithm proposed in 2016, reaching the optimal level of the theoretical lower bound.