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

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 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.

arXiv commentAccepted by Journal of Machine Learning Research (JMLR)

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Cited by in corpus (11)

• FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
• On Explainability of Graph Neural Networks via Subgraph Explorations
• Graph Neural Networks for Natural Language Processing: A Survey
• Automated Machine Learning on Graphs: A Survey
• Robust Counterfactual Explanations on Graph Neural Networks
• GraphDF: A Discrete Flow Model for Molecular Graph Generation
• A Network Science perspective of Graph Convolutional Networks: A survey
• LibAUC: A Deep Learning Library for X-Risk Optimization
• Instance-incremental Scene Graph Generation from Real-world Point Clouds via Normalizing Flows
• Explaining GNN over Evolving Graphs using Information Flow
• Graph Feature Gating Networks