Graph-structured data appears frequently in domains including chemistry, natural language
semantics, social networks, and knowledge bases. In this work, we study feature learning
techniques for graph-structured inputs. Our starting point is previous work on Graph Neural
Networks (Scarselli et al., 2009), which we modify to use gated recurrent units and modern
optimization techniques and then extend to output sequences. The result is a flexible and
broadly useful class of neural network models that has favorable inductive biases relative to …