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DGL

swMATH ID: 33907
Software Authors:
Description: Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks. 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.
Homepage: https://www.dgl.ai
Source Code:  https://github.com/dmlc/dgl
Keywords: Cluster Computing; arXiv_cs.DC; Machine Learning; arXiv_cs.LG; Deep Graph Library; Graph Neural Networks; GNN
Related Software: PyTorch; Python; PyG; TensorFlow; Adam; DropEdge; DIG; ImageNet; word2vec; MoleculeNet; NetworkX; Keras; Scikit; CIFAR; NumPy; LundNet; GitHub; Tensor2Tensor; PTE; Rdkit
Cited in: 10 Documents

Standard Articles

1 Publication describing the Software Year
Deep Graph Library Optimizations for Intel(R) x86 Architecture arXiv
Sasikanth Avancha, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty
2020

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