  Although there exist several libraries for deep learning on graphs, they are
aiming at implementing basic operations for graph deep learning. In the
research community, implementing and benchmarking various advanced tasks are
still painful and time-consuming with existing libraries. To facilitate graph
deep learning research, we introduce DIG: Dive into Graphs, a research-oriented
library that integrates unified and extensible implementations of common graph
deep learning algorithms for several advanced tasks. Currently, we consider
graph generation, self-supervised learning on graphs, explainability of graph
neural networks, and deep learning on 3D graphs. For each direction, we provide
unified implementations of data interfaces, common algorithms, and evaluation
metrics. Altogether, DIG is an extensible, open-source, and turnkey library for
researchers to develop new methods and effortlessly compare with common
baselines using widely used datasets and evaluation metrics. Source code and
documentations are available at https://github.com/divelab/DIG/.
