  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 turnkey library
that provides a unified testbed for higher level, research-oriented graph deep
learning 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 is available at
https://github.com/divelab/DIG.
