Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs
Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs
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发表时间：
2019-09
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通讯作者：
Minjie Wang;Lingfan Yu;Da Zheng;Quan Gan;Yujie Gai;Zihao Ye;Mufei Li;Jinjing Zhou;Qi Huang-
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作者：
Minjie Wang;Lingfan Yu;Da Zheng;Quan Gan;Yujie Gai;Zihao Ye;Mufei Li;Jinjing Zhou;Qi Huang-
加速深度图学习这一新兴领域的研究需要新的工具。这样的系统应该支持图形作为核心抽象，并注意保持向前（即支持新的研究思路）和向后（即与现有组件集成）的兼容性。本文提出了深度图库（Deep Graph Library， DGL）。DGL支持任意的消息处理和突变操作符，灵活的传播规则，并且与框架无关，从而利用现有框架中已有的高性能张量、自grad操作和其他特征提取模块。DGL仔细处理稀疏和不规则的图形结构，处理可能动态变化的大小图形，融合操作，自动批处理，都是为了利用现代硬件的优势。DGL已经在各种模型上进行了测试，包括但不限于流行的图神经网络（GNN）及其变体，具有良好的速度、内存占用和可扩展性。
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.