Graphs provide a natural framework for visualizing and analyzing networks of many types, including biological networks. Network clustering is a valuable approach for summarizing the structure in large networks, for predicting unobserved interactions, and for predicting functional annotations. Many current clustering algorithms suffer from a common set of limitations: poor resolution of top-level clusters, over-splitting of bottom-level clusters, requirements to pre-define the number of clusters prior to analysis, and an inability to jointly cluster over multiple interaction types.