GraphGuess: Approximate Graph Processing System with Adaptive Correction

TL;DR

GraphGuess is an approximate graph processing system with adaptive correction that significantly reduces processing time for large graphs while maintaining high accuracy, offering a practical trade-off between speed and precision.

Contribution

We introduce GraphGuess, a novel approximate graph processing system with adaptive correction, extending approximate graph theory to practical, scalable graph processing.

Findings

Reduces processing time for large graphs

Maintains high accuracy with approximation

Compatible with existing graph systems

Abstract

Graph-based data structures have drawn great attention in recent years. The large and rapidly growing trend on developing graph processing systems focuses mostly on improving the performance by preprocessing the input graph and modifying its layout. These systems usually take several hours to days to complete processing a single graph on high-end machines, let alone the overhead of pre-processing which most of the time can be dominant. Yet for most graph applications the exact answer is not always crucial, and providing a rough estimate of the final result is adequate. Approximate computing is introduced to trade off accuracy of results for computation or energy savings that could not be achieved by conventional techniques alone. In this work, we design, implement and evaluate GraphGuess, inspired from the domain of approximate graph theory and extend it to a general, practical graph processing system. GraphGuess is essentially an approximate graph processing technique with adaptive correction, which can be implemented on top of any graph processing system. We build a vertex-centric processing system based on GraphGuess, where it allows the user to trade off accuracy for better performance. Our experimental studies show that using GraphGuess can significantly reduce the processing time for large scale graphs while maintaining high accuracy.