GraphSnapShot: Caching Local Structure for Fast Graph Learning

TL;DR

GraphSnapShot is a framework that accelerates graph learning by efficiently caching and updating local graph structures, significantly reducing training time and memory usage in large dynamic graph applications.

Contribution

It introduces a novel caching framework for fast storage, retrieval, and computation of local graph structures, enabling significant speed and memory improvements.

Findings

Achieves up to 30% training acceleration.

Reduces memory usage by 73%.

Effective for large dynamic graphs.

Abstract

In our recent research, we have developed a framework called GraphSnapShot, which has been proven an useful tool for graph learning acceleration. GraphSnapShot is a framework for fast cache, storage, retrieval and computation for graph learning. It can quickly store and update the local topology of graph structure and allows us to track patterns in the structure of graph networks, just like take snapshots of the graphs. In experiments, GraphSnapShot shows efficiency, it can achieve up to 30% training acceleration and 73% memory reduction for lossless graph ML training compared to current baselines such as dgl.This technique is particular useful for large dynamic graph learning tasks such as social media analysis and recommendation systems to process complex relationships between entities. The code for GraphSnapShot is publicly available at https://github.com/NoakLiu/GraphSnapShot.