mlsanityData to 7 Oct 2026

TF-GNN: Graph Neural Networks in TensorFlow

arXiv 2207.03522 ↗First posted 7 Jul 2022cs.LG cs.NE cs.SIcited by 21

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Abstract (arXiv)

TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that occurs in today's information ecosystems. In addition to enabling machine learning researchers and advanced developers, TF-GNN offers low-code solutions to empower the broader developer community in graph learning. Many production models at Google use TF-GNN, and it has been recently released as an open source project. In this paper we describe the TF-GNN data model, its Keras message passing API, and relevant capabilities such as graph sampling and distributed training.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 32 of its 50 references that are in the map. First posted on arXiv 7 Jul 2022.

Builds on

32 of 50 references.

References · 32

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20 more
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Citing papers per year: 2022: 2; 2023: 7; 2024: 10; 2025: 2.

21 papers.

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  1. 2022MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initializationcited by 31
  2. 2023TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphscited by 16
  3. 2024Graph2Tac: Online Representation Learning of Formal Math Conceptscited by 15
  4. 2023Asynchronous Algorithmic Alignment with Cocyclescited by 13
  5. 2024GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applicationscited by 10
  6. 2023UGSL: A Unified Framework for Benchmarking Graph Structure Learningcited by 9
  7. 2025GiGL: Large-Scale Graph Neural Networks at Snapchatcited by 8
  8. 2024GraphStorm: all-in-one graph machine learning framework for industry applicationscited by 7
  9. 2023Optimizing ZX-Diagrams with Deep Reinforcement Learningcited by 6
  10. 2024Promoting Fairness in Link Prediction with Graph Enhancementcited by 6
  11. 2023Text Reading Order in Uncontrolled Conditions by Sparse Graph Segmentationcited by 5
  12. 2024Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modelingcited by 5
9 more
  1. 2022Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarkscited by 4
  2. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  3. 2025Training speedups via batching for geometric learning: an analysis of static and dynamic algorithmscited by 2
  4. 2023A Network Science perspective of Graph Convolutional Networks: A surveycited by 1
  5. 2024Optimization of GNN Training Through Half-precisioncited by 1
  6. 2023Greedy PIG: Adaptive Integrated Gradientscited by 0
  7. 2024Contrastive Representation Learning for Dynamic Link Prediction in Temporal Networkscited by 0
  8. 2024Fine Grained Insider Risk Detectioncited by 0
  9. 2024Graph Neural Networks on Graph Databasescited by 0