  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.
