EdgeSketch: Efficient Analysis of Massive Graph Streams

TL;DR

EdgeSketch is a novel compact graph sketching method enabling efficient, single-pass analysis of massive graph streams, significantly reducing memory and runtime while maintaining accuracy for key graph tasks.

Contribution

The paper introduces EdgeSketch, a new streaming graph sketch that provides unbiased estimators, supports direct algorithm implementation, and offers substantial efficiency improvements over existing methods.

Findings

Substantial memory savings compared to lossless methods

Runtime improvements demonstrated in experiments

Maintains reliable accuracy in graph analysis tasks

Abstract

We introduce EdgeSketch, a compact graph representation for efficient analysis of massive graph streams. EdgeSketch provides unbiased estimators for key graph properties with controllable variance and supports implementing graph algorithms on the stored summary directly. It is constructed in a fully streaming manner, requiring a single pass over the edge stream, while offline analysis relies solely on the sketch. We evaluate the proposed approach on two representative applications: community detection via the Louvain method and graph reconstruction through node similarity estimation. Experiments demonstrate substantial memory savings and runtime improvements over both lossless representations and prior sketching approaches, while maintaining reliable accuracy.