This thesis proposes an algorithm for the efficient detection of overlapping communities in large graphs. Only super-fast local algorithms like Louvain are really practical for very large datasets, but they tend to give hierarchical rather than overlapping partitions. We develop some techniques that let you get reasonable families of overlapping partitions while preserving most of the good properties of Louvain. We build off an advance in the efficient detection of separated communities, the multilevel Louvain method, and draw inspiration from the Wang-Landau efficiency improvement to Markov chain Monte Carlo sampling. Partitions are iteratively proposed by Louvain, with the internal edges of the best parts downweighted after each step. This suppresses the dominant parts in subsequent partitions, allowing alternative parts to appear. The result is an ensemble of parts describing the overlapping structure of the network.