Fast Error-Bounded Distance Distribution Computation (Extended Abstract)

Distance distributions have been widely applied in many real-world applications, e.g., graph analysis. Unfortunately, due to the large data volume and expensive distance computation, the exact distance distribution computation is excessively slow. Motivated by this, we present a novel approximate solution in this paper that (i) achieves error-bound guarantees and (ii) is generic to various distance measures. Our proposed method outperforms the baseline in terms of accuracy and efficiency when evaluating on three widely used distance measures with real-world datasets.