LiGNN: Graph Neural Networks at LinkedIn
Fedor Borisyuk Shihai He Yunbo Ouyang Morteza Ramezani Peng Du Xiaochen Hou Chengming Jiang Nitin Pasumarthy Priya Bannur Birjodh Tiwana Ping Liu Siddharth Dangi Daqi Sun Zhoutao Pei Xiao Shi Sirou Zhu Qianqi Shen Kuang-Hsuan Lee David Stein Baolei Li Haichao Wei Amol Ghoting Souvik Ghosh
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.