Many of the networks studied today are too large to visualize in their entirety. To understand what these networks "look like" we turn instead to exploratory analysis methods for detecting pattern and structure in network topologies. In this talk I will describe recent work on several methods that attempt to detect large-scale structural features such as clustering and hierarchy. The methods described range from simple spectral methods for clustering to quite general methods based on maximum likelihood techniques and mixture models. I will give a variety of illustrative applications throughout the talk.