DGL

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.


References in zbMATH (referenced in 11 articles , 1 standard article )

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  1. Wen Zhang, Xiangnan Chen, Zhen Yao, Mingyang Chen, Yushan Zhu, Hongtao Yu, Yufeng Huang, Zezhong Xu, Yajing Xu, Ningyu Zhang, Zonggang Yuan, Feiyu Xiong, Huajun Chen: NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs (2022) arXiv
  2. Chaoyu Guan, Ziwei Zhang, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, Wenwu Zhu: AutoGL: A Library for Automated Graph Learning (2021) arXiv
  3. Emmanouil Krasanakis, Symeon Papadopoulos, Ioannis Kompatsiaris, Andreas Symeonidis: pygrank: A Python Package for Graph Node Ranking (2021) arXiv
  4. Guillaume Jaume, Pushpak Pati, Valentin Anklin, Antonio Foncubierta, Maria Gabrani: HistoCartography: A Toolkit for Graph Analytics in Digital Pathology (2021) arXiv
  5. Mufei Li, Jinjing Zhou, Jiajing Hu, Wenxuan Fan, Yangkang Zhang, Yaxin Gu, George Karypis: DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science (2021) arXiv
  6. Šourek, Gustav; Železný, Filip; Kuželka, Ondřej: Beyond graph neural networks with lifted relational neural networks (2021)
  7. Vincent Mallet, Carlos Oliver, Jonathan Broadbent, William L. Hamilton, Jérôme Waldispühl: RNAglib: A Python Package for RNA 2.5D Graphs (2021) arXiv
  8. Xu, Mengjia: Understanding graph embedding methods and their applications (2021)
  9. Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang: CogDL: An Extensive Toolkit for Deep Learning on Graphs (2021) arXiv
  10. Sasikanth Avancha, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty: Deep Graph Library Optimizations for Intel(R) x86 Architecture (2020) arXiv
  11. Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang: DropEdge: Towards Deep Graph Convolutional Networks on Node Classification (2019) arXiv