This document summarizes research on learning representations of large-scale networks. It discusses how deep learning techniques can be used to learn low-dimensional embeddings that encode network structure and relationships between nodes. Convolutional and recurrent neural networks are proposed as methods to learn representations of entire networks by processing sequences of local neighborhood structures in an end-to-end fashion.