IntroductionΒΆ
DIG includes unified implementations of data interfaces, common algorithms, and evaluation metrics for several advanced tasks. Our goal is to enable researchers to easily implement and benchmark algorithms. Currently, we consider the following research directions.
Graph Augmentation:
dig.auggraph
Graph Generation:
dig.ggraph
Self-supervised Learning on Graphs:
dig.sslgraph
Explainability of Graph Neural Networks:
dig.xgraph
Deep Learning on 3D Graphs:
dig.threedgraph
Fair Graph Representations:
dig.fairgraph
We provide a hands-on tutorial for each direction to help you to get started with DIG:
You can also refer to our provided examples about how to use APIs in DIG.
