DIG：一个用于图深度学习研究的交钥匙库
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
摘要：
虽然存在几个用于图上的深度学习的库，但它们旨在实现图深度学习的基本操作。在研究社区中，使用现有库实现和基准测试各种高级任务仍然是痛苦和耗时的。为了促进图深度学习研究，我们引入了DIG：Dive into Graphs，这是一个交钥匙库，为更高级别的面向研究的图深度学习任务提供了统一的测试平台。目前，我们考虑图生成，图上的自监督学习，图神经网络的可解释性，以及3D图上的深度学习。对于每个方向，我们提供统一的数据接口，通用算法和评估指标的实现。总而言之，DIG是一个可扩展的，开源的和交钥匙的库，供研究人员开发新方法，并使用广泛使用的数据集和评估指标轻松地与常见基线进行比较。源代码可在https://github.com/divelab/DIG上获得。
英文摘要：
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning. In the research community, implementing and benchmarking various advanced tasks are still painful and time-consuming with existing libraries. To facilitate graph deep learning research, we introduce DIG: Dive into Graphs, a turnkey library that provides a unified testbed for higher level, research-oriented graph deep learning tasks. Currently, we consider graph generation, self-supervised learning on graphs, explainability of graph neural networks, and deep learning on 3D graphs. For each direction, we provide unified implementations of data interfaces, common algorithms, and evaluation metrics. Altogether, DIG is an extensible, open-source, and turnkey library for researchers to develop new methods and effortlessly compare with common baselines using widely used datasets and evaluation metrics. Source code is available at https://github.com/divelab/DIG.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
