# LiGNN: Graph Neural Networks at LinkedIn

arXiv: https://arxiv.org/abs/2402.11139 · HTML: https://mlsanity.com/paper/2402.11139/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 17 Feb 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Generating Wikipedia by Summarizing Long Sequences](https://mlsanity.com/paper/1801.10198/) (2018)
- [One weird trick for parallelizing convolutional neural networks](https://mlsanity.com/paper/1404.5997/) (2014)
- [Scaling Graph Neural Networks with Approximate PageRank](https://mlsanity.com/paper/2007.01570/) (2020)
- [Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey](https://mlsanity.com/paper/2005.07496/) (2020)
- [PyTorch-BigGraph: A Large-scale Graph Embedding System](https://mlsanity.com/paper/1903.12287/) (2019)
- [PinnerFormer: Sequence Modeling for User Representation at Pinterest](https://mlsanity.com/paper/2205.04507/) (2022)
- [Relational Deep Learning: Graph Representation Learning on Relational Databases](https://mlsanity.com/paper/2312.04615/) (2023)
- [Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation](https://mlsanity.com/paper/2012.07064/) (2020)
- [MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization](https://mlsanity.com/paper/2210.00102/) (2022)
- [Grale: Designing Networks for Graph Learning](https://mlsanity.com/paper/2007.12002/) (2020)
- [Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching](https://mlsanity.com/paper/2111.00926/) (2021)
- [A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation](https://mlsanity.com/paper/2112.02275/) (2021)
- [Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation](https://mlsanity.com/paper/2308.03470/) (2023)
- [LANNS: A Web-Scale Approximate Nearest Neighbor Lookup System](https://mlsanity.com/paper/2010.09426/) (2020)
- [adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Ads](https://mlsanity.com/paper/2302.01255/) (2023)
- [NxtPost: User to Post Recommendations in Facebook Groups](https://mlsanity.com/paper/2202.03645/) (2022)
- [HUGE: Huge Unsupervised Graph Embeddings with TPUs](https://mlsanity.com/paper/2307.14490/) (2023)

## Built on by (most cited first, 30 of 31)

- [Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights](https://mlsanity.com/paper/2406.10727/) (2024)
- [One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs](https://mlsanity.com/paper/2412.00315/) (2024)
- [GiGL: Large-Scale Graph Neural Networks at Snapchat](https://mlsanity.com/paper/2502.15054/) (2025)
- [Valid Conformal Prediction for Dynamic GNNs](https://mlsanity.com/paper/2405.19230/) (2024)
- [On the H"older Stability of Multiset and Graph Neural Networks](https://mlsanity.com/paper/2406.06984/) (2024)
- [Graph Neural Networks on Quantum Computers](https://mlsanity.com/paper/2405.17060/) (2024)
- [OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2504.17811/) (2025)
- [Simple yet Effective Graph Distillation via Clustering](https://mlsanity.com/paper/2505.20807/) (2025)
- [A Fair Evaluation of Graph Foundation Models for Node Property Prediction](https://mlsanity.com/paper/2606.24509/) (2026)
- [LinkedIn Post Embeddings: Industrial Scale Embedding Generation and Usage across LinkedIn](https://mlsanity.com/paper/2405.11344/) (2024)
- [LiNR: Model Based Neural Retrieval on GPUs at LinkedIn](https://mlsanity.com/paper/2407.13218/) (2024)
- [Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening](https://mlsanity.com/paper/2505.14033/) (2025)
- [Understanding Generalization in Node and Link Prediction](https://mlsanity.com/paper/2507.00927/) (2025)
- [A Scalable and Efficient Signal Integration System for Job Matching](https://mlsanity.com/paper/2507.09797/) (2025)
- [Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach](https://mlsanity.com/paper/2404.15354/) (2024)
- [Inductive Graph Representation Learning with Quantum Graph Neural Networks](https://mlsanity.com/paper/2503.24111/) (2025)
- [HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation](https://mlsanity.com/paper/2505.19020/) (2025)
- [Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks](https://mlsanity.com/paper/2508.20597/) (2025)
- [Generalizing GNNs with Tokenized Mixture of Experts](https://mlsanity.com/paper/2602.09258/) (2026)
- [Memory Is No Longer a Bottleneck: Memory-Efficient Graph Filtering for Scalable Collaborative Filtering](https://mlsanity.com/paper/2606.21540/) (2026)
- [Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks](https://mlsanity.com/paper/2406.07917/) (2024)
- [IRA: Adaptive Interest-aware Representation and Alignment for Personalized Multi-interest Retrieval](https://mlsanity.com/paper/2504.17529/) (2025)
- [Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIn](https://mlsanity.com/paper/2506.12700/) (2025)
- [Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations](https://mlsanity.com/paper/2506.20362/) (2025)
- [Enhancing Graph Neural Networks: A Mutual Learning Approach](https://mlsanity.com/paper/2510.19223/) (2025)
- [Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction](https://mlsanity.com/paper/2602.12613/) (2026)
- [k-Maximum Inner Product Attention for Graph Transformers and the Expressive Power of GraphGPS](https://mlsanity.com/paper/2604.03815/) (2026)
- [RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation](https://mlsanity.com/paper/2606.18379/) (2026)
- [Efficient Recommendations via Graph Coarsening and Label Propagation](https://mlsanity.com/paper/2607.22287/) (2026)
- [Scaling Graph Neural Networks for Friend Recommendation: Multi-Hash User Embeddings and Temporal Neighbor Sampling](https://mlsanity.com/paper/2608.27413/) (2026)

## Abstract (arXiv)

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.