The degree distribution of a real world network -- the number of links per node -- often follows a power law, with some hubs having many more links than traditional graph generation methods predict. For years, preferential attachment and growth have been the proposed mechanisms that lead to these scale free networks. However, the two sides of bipartite graphs like collaboration networks are usually not scale free, and are therefore not well-explained by these processes. Here we develop a bipartite extension to the Randomly Stopped Linking Model and show that mixtures of geometric distributions lead to power laws according to a Central Limit Theorem for distributions with high variance. The two halves of the actor-movie network are not scale free and can be represented by just 5 geometric distributions, but they combine to form a scale free actor-actor unipartite projection without preferential attachment or growth. This result supports our claim that scale free networks are the natural result of many Bernoulli trials with high variance of which preferential attachment and growth are only one example.