Popularity of data-driven software engineering has led to an increasing demand on the infrastructures to support efficient execution of tasks that require deeper source code analysis. While task optimization and parallelization are the adopted solutions, other research directions are less explored. We present collective program analysis (CPA), a technique for scaling large scale source code analysis by leveraging analysis specific similarity. Analysis specific similarity is about, whether two or more programs can be considered similar for a given analysis. The key idea of collective program analysis is to cluster programs based on analysis specific similarity, such that running the analysis on one candidate in each cluster is sufficient to produce the result for others. For determining the analysis specific similarity and for clustering analysis-equivalent programs, we use a sparse representation and a canonical labeling scheme. A sparse representation contains only the parts that are relevant for the analysis and the canonical labeling helps with finding isomorphic sparse representations. In a nutshell, two or more programs with same sparse representation must behave similarly for the given analysis. Our evaluation shows that for a variety of source code analysis tasks when run on a large dataset of programs, our technique is able to achieve substantial reduction in the analysis times; on average 69% when compared to baseline and on average 36% when compared to a prior technique. We also show that there exists a large amount of analysis-equivalent programs in large datasets for variety of analysis.