CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
The new v0.6 release updates the tutorials and adds more examples, such as GraphMAE, GraphMAE2, and BGRL.
Full Changelog: https://github.com/THUDM/cogdl/compare/v0.5.3...v0.6
The CogDL v0.5.3 release supports mixed-precision training by setting fp16=True and provides a basic example written by Jittor. It also updates the tutorial in the document, fixes downloading links of some datasets, and fixes potential bugs of operators.
Full Changelog: https://github.com/THUDM/cogdl/compare/v0.5.2...v0.5.3
The CogDL 0.5.2 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.
Full Changelog: https://github.com/THUDM/cogdl/compare/v0.5.1...v0.5.2
The CogDL 0.5.1 release adds fast operators including SpMM (cpu version) and scatter_max (cuda version). It also adds lots of datasets for node classification which can be found in this link.
Full Changelog: https://github.com/THUDM/cogdl/compare/v0.5.0...v0.5.1
The CogDL 0.5.0 release focuses on modular design and ease of use. It designs and implements a unified training loop for GNN, which introduces DataWrapper to help prepare the training/validation/test data and ModelWrapper to define the training/validation/test steps.
Full Changelog: https://github.com/THUDM/cogdl/compare/0.4.1...v0.5.0
The CogDL 0.5.0 release focuses on modular design and ease of use. It designs and implements a unified training loop for GNN, which introduces DataWrapper to help prepare the training/validation/test data and ModelWrapper to define the training/validation/test steps.
The CogDL 0.5.0 release focuses on modular design and ease of use. It designs and implements a unified training loop for GNN, which introduces DataWrapper to help prepare the training/validation/test data and ModelWrapper to define the training/validation/test steps.
A new release! 🎉🎉🎉 In the new v0.4.1 release, CogDL implements multiple deepgnn models and we also give a analysis of deepgnn in Chinese. Now CogDL. supports both reversible and actnn for memory efficiency to help build super deep GNNs. Come and have a try. BTW, we are glad to announce that we will give a tutorial on KDD 2021 in August. Please see this link for more details. 🎉
revgcn, revgat, revgen)l0fos, aff30, arxivvenue.A new major release! 🎉🎉🎉
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. 🎉
Data to Graph), edge_index from torch.Tensor to tuple(Tensor, Tensor). The inputs of each GNN are unified as one parameter graph.A new major release! 🎉🎉🎉 It provides a fast spmm operator to speed up GNN training. We also release the first version of CogDL paper in arXiv. In the paper, we introduce the design, the characteristics, the features, and the reproducible leaderboards. Welcome to join our slack!