mlsanityData to 14 Oct 2026

Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIn

arXiv 2506.12700 ↗First posted 15 Jun 2025cs.LGcited by 0

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Abstract (arXiv)

Notification recommendation systems are critical to driving user engagement on professional platforms like LinkedIn. Designing such systems involves integrating heterogeneous signals across domains, capturing temporal dynamics, and optimizing for multiple, often competing, objectives. Graph Neural Networks (GNNs) provide a powerful framework for modeling complex interactions in such environments. In this paper, we present a cross-domain GNN-based system deployed at LinkedIn that unifies user, content, and activity signals into a single, large-scale graph. By training on this cross-domain structure, our model significantly outperforms single-domain baselines on key tasks, including click-through rate (CTR) prediction and professional engagement. We introduce architectural innovations including temporal modeling and multi-task learning, which further enhance performance. Deployed in LinkedIn's notification system, our approach led to a 0.10% lift in weekly active users and a 0.62% improvement in CTR. We detail our graph construction process, model design, training pipeline, and both offline and online evaluations. Our work demonstrates the scalability and effectiveness of cross-domain GNNs in real-world, high-impact applications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 47 references that are in the map. First posted on arXiv 15 Jun 2025.

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26 of 47 references.

References · 26

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  6. 2019Graph Neural Networks for Social Recommendationcited by 714
  7. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 676
  8. 2016Cross-stitch Networks for Multi-task Learningcited by 467
  9. 2020Inductive Representation Learning on Temporal Graphscited by 462
  10. 2021Dynamic Neural Networks: A Surveycited by 408
  11. 2019Time2Vec: Learning a Vector Representation of Timecited by 213
  12. 2020Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A surveycited by 124
14 more
  1. 2022Transformer for Graphs: An Overview from Architecture Perspectivecited by 107
  2. 2018CoNet: Collaborative Cross Networks for Cross-Domain Recommendationcited by 81
  3. 2022A Survey of Graph Neural Networks for Social Recommender Systemscited by 65
  4. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 61
  5. 2024LiGNN: Graph Neural Networks at LinkedIncited by 31
  6. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 30
  7. 2023AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendationscited by 10
  8. 2025GiGL: Large-Scale Graph Neural Networks at Snapchatcited by 8
  9. 2023Multi-domain Recommendation with Embedding Disentangling and Domain Alignmentcited by 7
  10. 2022Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approachcited by 5
  11. 2024LinkSAGE: Optimizing Job Matching Using Graph Neural Networkscited by 5
  12. 2022Personalized PageRank Graph Attention Networkscited by 4
  13. 2024Heterogeneous Graph-based Framework with Disentangled Representations Learning for Multi-target Cross Domain Recommendationcited by 1
  14. 2024TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scalecited by 1

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