mlsanityData to 7 Oct 2026

Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

arXiv 1909.01315 ↗First posted 3 Sep 2019cs.LG stat.MLcited by 241

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Abstract (arXiv)

Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and implementation of Deep Graph Library (DGL). DGL distills the computational patterns of GNNs into a few generalized sparse tensor operations suitable for extensive parallelization. By advocating graph as the central programming abstraction, DGL can perform optimizations transparently. By cautiously adopting a framework-neutral design, DGL allows users to easily port and leverage the existing components across multiple deep learning frameworks. Our evaluation shows that DGL significantly outperforms other popular GNN-oriented frameworks in both speed and memory consumption over a variety of benchmarks and has little overhead for small scale workloads.

Key facts. Cited by 241 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2019.

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Citing papers per year: 2021: 64; 2022: 53; 2023: 50; 2024: 45; 2025: 16; 2026: 13.

241 papers.

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  1. 2021OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphscited by 328
  2. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 326
  3. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  4. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 154
  5. 2021From Canonical Correlation Analysis to Self-supervised Graph Neural Networkscited by 138
  6. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 108
  7. 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
  8. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  9. 2022Evaluating Explainability for Graph Neural Networkscited by 85
  10. 2021Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Frameworkcited by 69
  11. 2022MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Datasetcited by 59
  12. 2021Hierarchical Graph Representations in Digital Pathologycited by 58
38 more
  1. 2022ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Imagescited by 54
  2. 2021Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threadscited by 51
  3. 2021BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessingcited by 50
  4. 2023GSLB: The Graph Structure Learning Benchmarkcited by 49
  5. 2022TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discoverycited by 45
  6. 2021Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learningcited by 44
  7. 2024Knowledge Graph Enhanced Large Language Model Editingcited by 38
  8. 2023Simple and Asymmetric Graph Contrastive Learning without Augmentationscited by 37
  9. 2022Multi-modal Graph Learning for Disease Predictioncited by 34
  10. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  11. 2024Position: Categorical Deep Learning is an Algebraic Theory of All Architecturescited by 32
  12. 2021Heterogeneous Graph Representation Learning with Relation Awarenesscited by 31
  13. 2021Meta-Knowledge Transfer for Inductive Knowledge Graph Embeddingcited by 31
  14. 2021SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extractioncited by 30
  15. 2021Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Trackercited by 29
  16. 2022Learning Graph Neural Networks for Image Style Transfercited by 29
  17. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 29
  18. 2021Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspectivecited by 28
  19. 2022Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learningcited by 28
  20. 2023Sparse Training of Discrete Diffusion Models for Graph Generationcited by 26
  21. 2021Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networkscited by 24
  22. 2021Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphscited by 22
  23. 2023Deep Graph Reprogrammingcited by 22
  24. 2023Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embeddingcited by 21
  25. 2021HistoCartography: A Toolkit for Graph Analytics in Digital Pathologycited by 20
  26. 2022Self-Supervised Graph Structure Refinement for Graph Neural Networkscited by 20
  27. 2021The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphscited by 19
  28. 2021Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Settingcited by 19
  29. 2022MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networkscited by 19
  30. 2021Learning Hierarchical Graph Neural Networks for Image Clusteringcited by 18
  31. 2021Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphscited by 18
  32. 2022LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Predictioncited by 18
  33. 2022Encoding Concepts in Graph Neural Networkscited by 18
  34. 2021Efficient Graph Deep Learning in TensorFlow with tf_geometriccited by 17
  35. 2021Generalizable Cross-Graph Embedding for GNN-based Congestion Predictioncited by 17
  36. 2021TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUscited by 17
  37. 2023Continual Graph Learning: A Surveycited by 17
  38. 2022A Two-Stream AMR-enhanced Model for Document-level Event Argument Extractioncited by 16

50 most cited of 241 All 241 (JSON)