Introduction of DIG: Dive into Graphs ======
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 Generation:
dig.ggraph - Self-supervised Learning on Graphs:
dig.sslgraph - Explainability of Graph Neural Networks:
dig.xgraph - Deep Learning on 3D Graphs:
dig.threedgraph
You can refer to benchmark implementations as examples to use APIs provided in DIG.
