The All-Distances Sketch (ADS) is a powerful and theoretically-sound sketching scheme that captures neighborhood information in graphs for approximate processing. It enables high-accuracy estimation of many useful applications with a guarantee of accuracy and can significantly accelerate the execution times by orders of magnitude. However, ADS requires a substantial amount of space that is multiple times larger than the graph data. More seriously, existing studies mainly focus on managing ADSs in memory, posing an increasing challenge for users who aim to leverage ADS for large-scale graph processing, particularly in light of the exponential growth of real-world graphs nowadays. To this end, this paper introduces Oasis, an O ut-of-core A pproximate graph SYS tem that brings the ADS technique into practical use by leveraging storage effectively. Specifically, Oasis offers a holistic framework that facilitates both ADS construction and estimation. For ADS construction, it allows users to adjust the memory usage based on the machine’s available memory and enable an efficient construction process. For ADS estimation, Oasis provides a user-friendly interface to easily execute the estimators while mitigating the impact of slow storage I/O. Evaluation results show that Oasis provides a practical graph processing solution with exceptional execution time and low memory usage, at the cost of a slight decrease in accuracy.
Yang et al. (Fri,) studied this question.