Within the machine learning community, there has been a growing interest in learning structured models from input data that is itself structured. Graph identification refers to methods that transform an observed input graph into an inferred output graph. Examples include inferring organizational hierarchies from social network data and identifying gene regulatory networks from protein-protein interactions. The key processes in graph identification are entity resolution, link prediction, and collective classification. I will overview algorithms for these tasks and discuss the need for integrating the results to solve the overall problem collectively.