We describe methods for constructing and using content graphs for query-by-example speaker recognition tasks within a large speech corpus. This goal is achieved as follows: First, we describe an algorithm for constructing speaker content graphs, where nodes represent speech signals and edges represent speaker similarity. Speech signal similarity can be based on any standard vector-based speaker comparison method, and the content graph can be constructed using an efficient incremental method for streaming data. Second, we apply random walk methods to the content graph to find matching examples to an unlabeled query set of speech signals. The content-graph based method is contrasted to a more traditional approach that uses supervised training and stack detectors. Performance is compared in terms of information retrieval measures and computational complexity. The new content-graph based method is shown to provide a promising low-complexity scalable alternative to standard speaker recognition methods.