# Why CogDL?
CogDL is an extensive research toolkit for deep learning on graphs that allows researchers and developers to easily conduct experiments and build applications. It provides standard training and evaluation for the most important tasks in the graph domain, including node classification, link prediction, graph classification, and other graph tasks.
For each task, it offers implementations of state-of-the-art models. The models in our toolkit are divided into two major parts, graph embedding methods and graph neural networks. Most of the graph embedding methods learn node-level or graph-level representations in an unsupervised way and preserves the graph properties such as structural information, while graph neural networks capture node features and work in semi-supervised or self-supervised settings.
All models implemented in our toolkit can be easily reproducible for leaderboard results. Most models in CogDL are developed on top of PyTorch, and users can leverage the advantages of PyTorch to implement their own models.
# CogDL Team
# Core Development
The core development team can be reached at firstname.lastname@example.org.
Tsinghua University: Yukuo Cen, Zhenyu Hou, Yizhen Luo, Shiguang Guo, Zhongming Yu
# Steering Committee
Tsinghua University: Guohao Dai, Yu Wang, Jie Tang
DAMO Academy, Alibaba Group: Chang Zhou, Hongxia Yang
ZHIPU.AI: Peng Zhang