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DGL

swMATH ID: 33907
Software Authors: Minjie Wang, Da Zheng, Guan Gan, Mufei Li, Zihao Ye, Chao Ma, Jinjing Zhou, Xiang Song, Tianjun Xiao, Tong He, Jian Zhang, Wen-ming Ye, George Karypis, Zheng Zhang
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
Keywords: Cluster Computing; arXiv_cs.DC; Machine Learning; arXiv_cs.LG; Deep Graph Library; Graph Neural Networks; GNN
Related Software: PyTorch; Python; MoleculeNet; Keras; TensorFlow; RotatE; Freebase; NetworkX; GraphSAGE; DropEdge; HIN2Vec; metapath2vec; GraRep; node2vec; word2vec; MolGAN; Retip; MolGraph; DGL-KE; Pykg2vec
Referenced in: 3 Publications

Standard Articles

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

Referencing Publications by Year