CogDL provides easy-to-use APIs for running experiments with the given models and datasets using hyper-parameter search.
CogDL provides reproducible leaderboards for state-of-the-art models on most of important tasks in the graph domain.
CogDL utilizes well-optimized operators to speed up training and save GPU memory of GNN models.
The new v0.4 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.
The new v0.2 release includes easy-to-use experiment and pipeline APIs for all experiments and applications. The experiment API supports automl features of searching hyper-parameters. Thanks to all the contributors. 🎉
CogDL provides OAGBert API for model inference (OAGBert is trained on large-scale academic corpus). Details of OAGBert usage can be found in this link.