EBSim: the algorithm for approximate single pair queries
Yinghao Zhao, Yu Liu · 2024
This paper proposes an algorithm strategy that utilizes the empirical Bernstein inequality to reduce the number of random walk samples in the single pair algorithm, thereby lowering the algorithm's theoretical complexity. An algorithm named EBSim is introduced, which combines the empirical Bernstein inequality by iteratively adjusting the number of samples, and it is proven to have strict error guarantees. The paper also provides a detailed analysis comparing this algorithm with the Monte Carlo algorithm, demonstrating that our algorithm achieves tighter confidence intervals, lower algorithm complexity, and higher efficiency in handling the same query problems. Experimental results show that, compared to the widely used Monte Carlo query algorithm, EBSim significantly improves algorithm efficiency while ensuring a given error tolerance.