github.com/dmlc/dgl @v2.4.0 sqlite

Website | A Blitz Introduction to DGL | Documentation (Latest | Stable) | Official Examples | Discussion Forum | Slack Channel

DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow.

Figure: DGL Overall Architecture

Highlighted Features

A GPU-ready graph library

DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks.

A versatile tool for GNN researchers and practitioners

The field of graph deep learning is still rapidly evolving and many research ideas emerge by standing on the shoulders of giants. To ease the process, DGl-Go is a command-line interface to get started with training, using and studying state-of-the-art GNNs. DGL collects a rich set of example implementations of popular GNN models of a wide range of topics. Researchers can search for related models to innovate new ideas from or use them as baselines for experiments. Moreover, DGL provides many state-of-the-art GNN layers and modules for users to build new model architectures. DGL is one of the preferred platforms for many standard graph deep learning benchmarks including OGB and GNNBenchmarks.

Easy to learn and use

DGL provides plenty of learning materials for all kinds of users from ML researchers to domain experts. The Blitz Introduction to DGL is a 120-minute tour of the basics of graph machine learning. The User Guide explains in more details the concepts of graphs as well as the training methodology. All of them include code snippets in DGL that are runnable and ready to be plugged into one’s own pipeline.

Scalable and efficient

It is convenient to train models using DGL on large-scale graphs across multiple GPUs or multiple machines. DGL extensively optimizes the whole stack to reduce the overhead in communication, memory consumption and synchronization. As a result, DGL can easily scale to billion-sized graphs. Get started with the tutorials and user guide for distributed training. See the system performance note for the comparison with other tools.

Get Started

Users can install DGL from pip and conda. You can also download GPU enabled DGL docker containers (backended by PyTorch) from NVIDIA NGC for both x86 and ARM based linux systems. Advanced users can follow the instructions to install from source.

For absolute beginners, start with the Blitz Introduction to DGL. It covers the basic concepts of common graph machine learning tasks and a step-by-step on building Graph Neural Networks (GNNs) to solve them.

For acquainted users who wish to learn more,

• Experience state-of-the-art GNN models in only two command-lines using DGL-Go.
• Learn DGL by example implementations of popular GNN models.
• Read the User Guide (中文版链接), which explains the concepts and usage of DGL in much more details.
• Go through the tutorials for advanced features like stochastic training of GNNs, training on multi-GPU or multi-machine.
• Study classical papers on graph machine learning alongside DGL.
• Search for the usage of a specific API in the API reference manual, which organizes all DGL APIs by their namespace.

All the learning materials are available at our documentation site. If you are new to deep learning in general, check out the open source book Dive into Deep Learning.

Community

Get connected

We provide multiple channels to connect you to the community of the DGL developers, users, and the general GNN academic researchers:

• Our Slack channel, click to join
• Our discussion forum: https://discuss.dgl.ai/
• Our Zhihu blog (in Chinese)
• Monthly GNN User Group online seminar (event link | past videos)

Take the survey here and leave any feedback to make DGL better fit for your needs. Thanks!

DGL-powered projects

• DGL-LifeSci: a DGL-based package for various applications in life science with graph neural networks. https://github.com/awslabs/dgl-lifesci
• DGL-KE: a high performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings. https://github.com/awslabs/dgl-ke
• Benchmarking GNN: https://github.com/graphdeeplearning/benchmarking-gnns
• OGB: a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. https://ogb.stanford.edu/
• Graph4NLP: an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing. https://github.com/graph4ai/graph4nlp
• GNN-RecSys: https://github.com/je-dbl/GNN-RecSys
• Amazon Neptune ML: a new capability of Neptune that uses Graph Neural Networks (GNNs), a machine learning technique purpose-built for graphs, to make easy, fast, and more accurate predictions using graph data. https://aws.amazon.com/cn/neptune/machine-learning/
• GNNLens2: Visualization tool for Graph Neural Networks. https://github.com/dmlc/GNNLens2
• RNAGlib: A package to facilitate construction, analysis, visualization and machine learning on RNA 2.5D Graphs. Includes a pre-built dataset: https://rnaglib.cs.mcgill.ca
• OpenHGNN: Model zoo and benchmarks for Heterogeneous Graph Neural Networks. https://github.com/BUPT-GAMMA/OpenHGNN
• TGL: A graph learning framework for large-scale temporal graphs. https://github.com/amazon-research/tgl
• gtrick: Bag of Tricks for Graph Neural Networks. https://github.com/sangyx/gtrick
• ArangoDB-DGL Adapter: Import ArangoDB graphs into DGL and vice-versa. https://github.com/arangoml/dgl-adapter
• DGLD: DGLD is an open-source library for Deep Graph Anomaly Detection based on pytorch and DGL.

Awesome Papers Using DGL

1. Benchmarking Graph Neural Networks, Vijay Prakash Dwivedi, Chaitanya K. Joshi, Thomas Laurent, Yoshua Bengio, Xavier Bresson

2. Open Graph Benchmarks: Datasets for Machine Learning on Graphs, NeurIPS'20, Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec

3. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification, ICLR'20, Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huan

4. Discourse-Aware Neural Extractive Text Summarization, ACL'20, Jiacheng Xu, Zhe Gan, Yu Cheng, Jingjing Liu

5. GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training, KDD'20, Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang

6. DGL-KE: Training Knowledge Graph Embeddings at Scale, SIGIR'20, Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, George Karypis

7. Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting, Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, Michael M. Bronstein

8. INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving, Yuhuai Wu, Albert Q. Jiang, Jimmy Ba, Roger Grosse

9. Finding Patient Zero: Learning Contagion Source with Graph Neural Networks, Chintan Shah, Nima Dehmamy, Nicola Perra, Matteo Chinazzi, Albert-László Barabási, Alessandro Vespignani, Rose Yu

10. FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems, SC'20, Yuwei Hu, Zihao Ye, Minjie Wang, Jiali Yu, Da Zheng, Mu Li, Zheng Zhang, Zhiru Zhang, Yida Wang

more

1. BP-Transformer: Modelling Long-Range Context via Binary Partitioning., Zihao Ye, Qipeng Guo, Quan Gan, Xipeng Qiu, Zheng Zhang

2. OptiMol: Optimization of Binding Affinities in Chemical Space for Drug Discovery, Jacques Boitreaud,Vincent Mallet, Carlos Oliver, Jérôme Waldispühl

3. JAKET: Joint Pre-training of Knowledge Graph and Language Understanding, Donghan Yu, Chenguang Zhu, Yiming Yang, Michael Zeng

4. Architectural Implications of Graph Neural Networks, Zhihui Zhang, Jingwen Leng, Lingxiao Ma, Youshan Miao, Chao Li, Minyi Guo

5. Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization, Quentin Cappart, Thierry Moisan, Louis-Martin Rousseau1, Isabeau Prémont-Schwarz, and Andre Cire

6. Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics (code repo), Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik

7. Sparse Graph Attention Networks, Yang Ye, Shihao Ji

8. On Self-Distilling Graph Neural Network, Yuzhao Chen, Yatao Bian, Xi Xiao, Yu Rong, Tingyang Xu, Junzhou Huang

9. Learning Robust Node Representations on Graphs, Xu Chen, Ya Zhang, Ivor Tsang, and Yuangang Pan

10. Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs, Woojeong Jin, Meng Qu, Xisen Jin, Xiang Ren

11. Graph Neural Ordinary Differential Equations, Michael Poli, Stefano Massaroli, Junyoung Park, Atsushi Yamashita, Hajime Asama, Jinkyoo Park

12. FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks, Md. Khaledur Rahman, Majedul Haque Sujon, , Ariful Azad

13. An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph, KDD'20 Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Weinan Zhang, Yong Yu, Zheng Zhang, Alexander J. Smola

14. Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network, Jiarui Jin, Kounianhua Du, Weinan Zhang, Jiarui Qin, Yuchen Fang, Yong Yu, Zheng Zhang, Alexander J. Smola

15. Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures, *Arian R. Jamasb, Pietro Lió,