LiGNN: Graph Neural Networks at LinkedIn Article Swipe
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
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.11139
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.11139
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.
Related Topics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2402.11139
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- OA Status
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- Related Works
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- OpenAlex ID
- https://openalex.org/W4392011940
All OpenAlex metadata
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https://openalex.org/W4392011940Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2402.11139Digital Object Identifier
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LiGNN: Graph Neural Networks at LinkedInWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-02-17Full publication date if available
- Authors
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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 GhoshList of authors in order
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https://arxiv.org/abs/2402.11139Publisher landing page
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https://arxiv.org/pdf/2402.11139Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2402.11139Direct OA link when available
- Concepts
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Graph, Computer science, Theoretical computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.start | 53 |
| abstract_inverted_index.(GNNs) | 12 |
| abstract_inverted_index.LiGNN, | 5 |
| abstract_inverted_index.Neural | 10 |
| abstract_inverted_index.active | 141, 149 |
| abstract_inverted_index.graphs | 79 |
| abstract_inverted_index.neural | 174 |
| abstract_inverted_index.paper, | 2 |
| abstract_inverted_index.people | 153 |
| abstract_inverted_index.scale. | 178 |
| abstract_inverted_index.users, | 142 |
| abstract_inverted_index.weekly | 148 |
| abstract_inverted_index.believe | 156 |
| abstract_inverted_index.engaged | 139 |
| abstract_inverted_index.explain | 65 |
| abstract_inverted_index.hearing | 129 |
| abstract_inverted_index.insight | 17 |
| abstract_inverted_index.lessons | 103 |
| abstract_inverted_index.losses, | 50 |
| abstract_inverted_index.present | 4, 30 |
| abstract_inverted_index.provide | 161 |
| abstract_inverted_index.quality | 38 |
| abstract_inverted_index.session | 144 |
| abstract_inverted_index.slicing | 87 |
| abstract_inverted_index.LinkedIn | 78 |
| abstract_inverted_index.Networks | 11 |
| abstract_inverted_index.adaptive | 81 |
| abstract_inverted_index.applying | 172 |
| abstract_inverted_index.batches, | 91 |
| abstract_inverted_index.deployed | 7 |
| abstract_inverted_index.gathered | 106 |
| abstract_inverted_index.gradient | 97 |
| abstract_inverted_index.grouping | 85 |
| abstract_inverted_index.insights | 165 |
| abstract_inverted_index.learning | 42 |
| abstract_inverted_index.neighbor | 62 |
| abstract_inverted_index.networks | 175 |
| abstract_inverted_index.relative | 122 |
| abstract_inverted_index.sampling | 82 |
| abstract_inverted_index.temporal | 44 |
| abstract_inverted_index.training | 76, 89 |
| abstract_inverted_index.LinkedIn. | 28 |
| abstract_inverted_index.effective | 51 |
| abstract_inverted_index.engineers | 167 |
| abstract_inverted_index.including | 43 |
| abstract_inverted_index.learnings | 105 |
| abstract_inverted_index.multi-hop | 61 |
| abstract_inverted_index.practical | 162 |
| abstract_inverted_index.presented | 113 |
| abstract_inverted_index.sampling. | 63 |
| abstract_inverted_index.solutions | 54, 163 |
| abstract_inverted_index.summarize | 100 |
| abstract_inverted_index.Framework. | 13 |
| abstract_inverted_index.deployment | 21, 102 |
| abstract_inverted_index.developing | 19 |
| abstract_inverted_index.embeddings | 59 |
| abstract_inverted_index.interested | 170 |
| abstract_inverted_index.neighbors, | 84 |
| abstract_inverted_index.techniques | 112 |
| abstract_inverted_index.algorithmic | 34 |
| abstract_inverted_index.application | 128 |
| abstract_inverted_index.approximate | 121 |
| abstract_inverted_index.contributed | 118 |
| abstract_inverted_index.large-scale | 8, 75 |
| abstract_inverted_index.specialized | 92 |
| abstract_inverted_index.experiments. | 110 |
| abstract_inverted_index.improvements | 35, 123 |
| abstract_inverted_index.architectures | 46 |
| abstract_inverted_index.optimization. | 98 |
| abstract_inverted_index.shared-memory | 93 |
| abstract_inverted_index.densification, | 57 |
| abstract_inverted_index.representation | 41 |
| abstract_inverted_index.recommendation. | 154 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 23 |
| citation_normalized_percentile |