DIG: A Turnkey Library for Diving into Graph Deep Learning Research

• URL: http://arxiv.org/abs/2103.12608v1
• Date: Tue, 23 Mar 2021 15:05:10 GMT
• Title: DIG: A Turnkey Library for Diving into Graph Deep Learning Research
• Authors: Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Zhao Xu, Haiyang Yu, Jingtun Zhang, Yi Liu, Keqiang Yan, Bora Oztekin, Haoran Liu, Xuan Zhang, Cong Fu, Shuiwang Ji
• Abstract summary: DIG: Dive into Graphs is a research-oriented library that integrates and unified implementations of common graph deep learning algorithms for several advanced tasks. For each direction, we provide unified implementations of data interfaces, common algorithms, and evaluation metrics.
• Score: 39.58666190541479
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 research-oriented library that integrates unified and extensible implementations of common graph deep learning algorithms for several advanced 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 and documentations are available at https://github.com/divelab/DIG/.

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