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Upcoming events (1)
See all- LiGNN: Graph Neural Networks at LinkedInLink visible for attendees
Mansour Sow will be our host for this session of the reading group where we study LiGNN. You can find below the abstract of the article .
Abstract: In this paper, we present LiGNN, a deployed large-scale Graph
Neural Networks (GNNs) Framework. We share our insight on developing 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 densification, 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 approximate 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 recommendation. We believe that this work can provide practical solutions
and insights for engineers who are interested in applying Graph
neural networks at large scale.
