Welcome to CogDL’s Documentation!


CogDL is a graph representation learning toolkit that allows researchers and developers to easily train and compare baseline or customized models for node classification, graph classification, and other important tasks in the graph domain.

We summarize the contributions of CogDL as follows:

  • Efficiency: CogDL utilizes well-optimized operators to speed up training and save GPU memory of GNN models.

  • Ease of Use: CogDL provides easy-to-use APIs for running experiments with the given models and datasets using hyper-parameter search.

  • Extensibility: The design of CogDL makes it easy to apply GNN models to new scenarios based on our framework.

❗ News

  • [The CogDL paper](https://arxiv.org/abs/2103.00959) was accepted by [WWW 2023](https://www2023.thewebconf.org/). Find us at WWW 2023! We also release the new v0.6 release which adds more examples of graph self-supervised learning, including [GraphMAE](https://github.com/THUDM/cogdl/tree/master/examples/graphmae), [GraphMAE2](https://github.com/THUDM/cogdl/tree/master/examples/graphmae2), and [BGRL](https://github.com/THUDM/cogdl/tree/master/examples/bgrl).

  • The new v0.5.3 release supports mixed-precision training by setting textit{fp16=True} and provides a basic [example](https://github.com/THUDM/cogdl/blob/master/examples/jittor/gcn.py) written by [Jittor](https://github.com/Jittor/jittor). It also updates the tutorial in the document, fixes downloading links of some datasets, and fixes potential bugs of operators.

  • The new v0.5.2 release adds a GNN example for ogbn-products and updates geom datasets. It also fixes some potential bugs including setting devices, using cpu for inference, etc.

  • The new v0.5.1 release adds fast operators including SpMM (cpu version) and scatter_max (cuda version). It also adds lots of datasets for node classification. 🎉

  • The new v0.5.0 release designs and implements a unified training loop for GNN. It introduces DataWrapper to help prepare the training/validation/test data and ModelWrapper to define the training/validation/test steps.

  • The new v0.4.1 release adds the implementation of Deep GNNs and the recommendation task. It also supports new pipelines for generating embeddings and recommendation. Welcome to join our tutorial on KDD 2021 at 10:30 am - 12:00 am, Aug. 14th (Singapore Time). More details can be found in https://kdd2021graph.github.io/. 🎉

  • The new v0.4.0 release refactors the data storage (from Data to Graph) and provides more fast operators to speed up GNN training. It also includes many self-supervised learning methods on graphs. BTW, we are glad to announce that we will give a tutorial on KDD 2021 in August. Please see this link for more details. 🎉

  • The new v0.3.0 release provides a fast spmm operator to speed up GNN training. We also release the first version of CogDL paper in arXiv. You can join our slack for discussion. 🎉🎉🎉

  • The new v0.2.0 release includes easy-to-use experiment and pipeline APIs for all experiments and applications. The experiment API supports automl features of searching hyper-parameters. This release also provides OAGBert API for model inference (OAGBert is trained on large-scale academic corpus by our lab). Some features and models are added by the open source community (thanks to all the contributors 🎉).

  • The new v0.1.2 release includes a pre-training task, many examples, OGB datasets, some knowledge graph embedding methods, and some graph neural network models. The coverage of CogDL is increased to 80%. Some new APIs, such as Trainer and Sampler, are developed and being tested.

  • The new v0.1.1 release includes the knowledge link prediction task, many state-of-the-art models, and optuna support. We also have a Chinese WeChat post about the CogDL release.

Citing CogDL

Please cite our paper if you find our code or results useful for your research:

   title={CogDL: A Toolkit for Deep Learning on Graphs},
   author={Yukuo Cen and Zhenyu Hou and Yan Wang and Qibin Chen and Yizhen Luo and Zhongming Yu and Hengrui Zhang and Xingcheng Yao and Aohan Zeng and Shiguang Guo and Yuxiao Dong and Yang Yang and Peng Zhang and Guohao Dai and Yu Wang and Chang Zhou and Hongxia Yang and Jie Tang},
   journal={arXiv preprint arXiv:2103.00959},

Indices and tables