We propose GNNLab, a sample-based GNN training system in a single machine multi-GPU
setup. GNNLab adopts a factored design for multiple GPUs, where each GPU is dedicated to …

Graph Convolutional Networks (GCNs) are increasingly adopted in large-scale graph-based
recommender systems. Training GCN requires the minibatch generator traversing graphs …

Graph neural networks (GNNs) can extract features by learning both the representation of
each objects (ie, graph nodes) and the relationship across different objects (ie, the edges …

Subgraph-based graph representation learning (SGRL) has been recently proposed to deal
with some fundamental challenges encountered by canonical graph neural networks …

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 …

The last few years witnessed significant advances in building automated or semi-automated
data quality, data cleaning and data integration systems powered by machine learning (ML) …

Training and inference with graph neural networks (GNNs) on massive graphs in a
distributed environment has been actively studied since the inception of GNNs, owing to the …

Edge intelligence has arisen as a promising computing paradigm for supporting
miscellaneous smart applications that rely on machine learning techniques. While the …

We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the
premise of using distributed training for billion-scale graphs and show that for graphs that fit …

We introduce Saga, a next-generation knowledge construction and serving platform for
powering knowledge-based applications at industrial scale. Saga follows a hybrid batch …