Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs
Abstrak
Accelerating research in the emerging field of deep graph learning requires new tools. Such systems should support graph as the core abstraction and take care to maintain both forward (i.e. supporting new research ideas) and backward (i.e. integration with existing components) compatibility. In this paper, we present Deep Graph Library (DGL). DGL enables arbitrary message handling and mutation operators, flexible propagation rules, and is framework agnostic so as to leverage high-performance tensor, autograd operations, and other feature extraction modules already available in existing frameworks. DGL carefully handles the sparse and irregular graph structure, deals with graphs big and small which may change dynamically, fuses operations, and performs auto-batching, all to take advantages of modern hardware. DGL has been tested on a variety of models, including but not limited to the popular Graph Neural Networks (GNN) and its variants, with promising speed, memory footprint and scalability.
Topik & Kata Kunci
Penulis (18)
Minjie Wang
Lingfan Yu
Da Zheng
Quan Gan
Yujie Gai
Zihao Ye
Mufei Li
Jinjing Zhou
Qi Huang
Chao Ma
Ziyue Huang
Qipeng Guo
Haotong Zhang
Haibin Lin
J. Zhao
Jinyang Li
Alex Smola
Zheng Zhang
Akses Cepat
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Cek di sumber asli →- Tahun Terbit
- 2019
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- Semantic Scholar
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- Open Access ✓