mlsanityData to 7 Oct 2026

GraphStorm: all-in-one graph machine learning framework for industry applications

arXiv 2406.06022 ↗First posted 10 Jun 2024cs.LG cs.DCcited by 7

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Abstract (arXiv)

Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution for scalable graph construction, graph model training and inference. GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code. GraphStorm has been used and deployed for over a dozen billion-scale industry applications after its release in May 2023. It is open-sourced in Github: https://github.com/awslabs/graphstorm.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 22 of its 31 references that are in the map. First posted on arXiv 10 Jun 2024.

Builds on

22 of 31 references.

References · 22

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  5. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  6. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  7. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  8. 2015Image-based Recommendations on Styles and Substitutescited by 702
  9. 2020Heterogeneous Graph Transformercited by 581
  10. 2022Learning on Large-scale Text-attributed Graphs via Variational Inferencecited by 153
  11. 2019AliGraph: A Comprehensive Graph Neural Network Platformcited by 152
  12. 2021Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillationcited by 151
10 more
  1. 2020DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphscited by 139
  2. 2019PyTorch-BigGraph: A Large-scale Graph Embedding Systemcited by 118
  3. 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
  4. 2019Relational Graph Attention Networkscited by 81
  5. 2020DGL-KE: Training Knowledge Graph Embeddings at Scalecited by 67
  6. 2022TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphscited by 59
  7. 2021Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoodscited by 57
  8. 2023Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applicationscited by 33
  9. 2022TF-GNN: Graph Neural Networks in TensorFlowcited by 21
  10. 2021CogDL: A Comprehensive Library for Graph Deep Learningcited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 2.

7 papers.

  1. 2025GiGL: Large-Scale Graph Neural Networks at Snapchatcited by 8
  2. 2024Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Studycited by 4
  3. 2025OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learningcited by 4
  4. 2025SparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operationscited by 0
  5. 2025Higher-Order Graph Databasescited by 0
  6. 2026Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelinescited by 0
  7. 2026Scaling Graph Neural Networks for Friend Recommendation: Multi-Hash User Embeddings and Temporal Neighbor Samplingcited by 0