  Advancing research in the emerging field of deep graph learning requires new
tools to support tensor computation over graphs. In this paper, we present the
design principles and implementation of Deep Graph Library (DGL). DGL distills
the computational patterns of GNNs into a few generalized sparse tensor
operations suitable for extensive parallelization. By advocating graph as the
central programming abstraction, DGL can perform optimizations transparently.
By cautiously adopting a framework-neutral design, DGL allows users to easily
port and leverage the existing components across multiple deep learning
frameworks. Our evaluation shows that DGL significantly outperforms other
popular GNN-oriented frameworks in both speed and memory consumption over a
variety of benchmarks and has little overhead for small scale workloads.
