The importance of network analysis is growing across many domains, and is fundamental in understanding online social interactions, biological processes, communication, ecological, financial, transportation networks, and more. In most of these domains, the networks of interest are not directly observed, but must be inferred from noisy and incomplete data, data that was often generated for purposes other than scientific analysis. In this talk, I will introduce the problem of graph identification, the process of inferring the hidden network from noisy observational data. I will describe some of the component steps involved, and then I will describe a collective approach to graph identification, which interleaves the necessary steps in the accurate reconstruction of the network. Joint work with Galileo Namata and Stanley Kok, University of Maryland.