LiGNN: Graph Neural Networks at LinkedIn

Fedor Borisyuk

Abstract:

In this paper, we present LiGNN , a deployed large-scale Graph
Neural Networks (GNNs) Framework. We share our insight on de-
veloping and deployment of GNNs at large scale at LinkedIn. We
present a set of algorithmic improvements to the quality of GNN
representation learning including temporal graph architectures
with long term losses, effective cold start solutions via graph den-
sification, ID embeddings and multi-hop neighbor sampling. We
explain how we built and sped up by 7x our large-scale training on
LinkedIn graphs with adaptive sampling of neighbors, grouping and
slicing of training data batches, specialized shared-memory queue
and local gradient optimization. We summarize our deployment
lessons and learnings gathered from A/B test experiments. The
techniques presented in this work have contributed to an approxi-
mate relative improvements of 1% of Job application hearing back
rate, 2% Ads CTR lift, 0.5% of Feed engaged daily active users, 0.2%
session lift and 0.1% weekly active user lift from people recommen-
dation. We believe that this work can provide practical solutions
and insights for engineers who are interested in applying Graph
neural networks at large scale.