Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet.You can see it in https://github.com/deepmind/graph_nets
I have implemented Graph Nets by Pytorch framework. You can see my work in https://github.com/TQCAI/graph_nets_pytorch
A graph network takes a graph as input and returns a graph as output. The input graph has edge- (E ), node- (V ), and global-level (u) attributes. The output graph has the same structure, but updated attributes. Graph networks are part of the broader family of "graph neural networks" (Scarselli et al., 2009).
To learn more about graph networks, see our arXiv paper: Relational inductive biases, deep learning, and graph networks.
You can see a forward calculation in demo.py