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The identification of substantively similar policy proposals in legislation is important to scholars of public policy and legislative politics. Manual approaches are prohibitively costly in constructing datasets that accurately represent policymaking across policy domains, jurisdictions, or time. We propose the use of an algorithm that identifies similar sequences of text (i.e., text reuse), applied to legislative text, to measure the similarity of the policy proposals advanced by two bills. We study bills from U.S. state legislatures. We present three ground truth tests, applied to a corpus of 500,000 bills. First, we show that bills introduced by ideologically similar sponsors exhibit a high degree of text reuse, that bills classified by the National Conference of State Legislatures as covering the same policies exhibit a high degree of text reuse, and that rates of text reuse between states correlate with policy diffusion network ties between states. In an empirical application of our similarity measure, we find that Republican state legislators introduce legislation that is more similar to legislation introduced by Republicans in other states, than is legislation introduced by Democratic state legislators to legislation introduced by Democrats in other states.

Many real networks that are collected or inferred from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access to a portion of the data). The consequence is that downstream analyses that “consume” the network will often yield less accurate results than if the edges were complete. Community detection algorithms, in particular, often suffer when critical intra-community edges are missing. We propose a novel consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that process networks imputed by our link prediction based sampling algorithm and merges their multiple partitions into a final consensus output. On average our method boosts performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook.

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