  Graph representation learning aims to learn low-dimensional node embeddings
for graphs. It is used in several real-world applications such as social
network analysis and large-scale recommender systems. In this paper, we
introduce CogDL, 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. Furthermore, we demonstrate the
effectiveness of CogDL for real-world applications in AMiner, which is a large
academic database and system.
