Common intuition posits that deep learning has succeeded because of its ability to assume very little structure in the data it receives, instead learning that structure from large numbers of training examples. However, recent work has attempted to bring structure back into deep learning, via a new set of models known as "graph networks". Graph networks allow for "relational inductive biases" to be introduced into learning, ie. explicit reasoning about relationships between entities. In this talk, I will introduce graph networks and one application of them to a physical reasoning task where an agent and human participants were asked to glue together pairs of blocks to stabilize a tower. We will go through DeepMind's recently released graph networks library (implemented in tensorflow) to see how to set up different graph models, and train some simple models on some simple tasks.