DIG (Dive Into Graphs) is a research library for graph deep learning that provides modular implementations of graph neural networks, graph generation models, self-supervised learning methods, and explainability techniques for graph-structured data. It includes benchmarks, tutorials, and example implementations to support research and development in graph representation learning.