  We present a novel approach for finding multiple noisily embedded template
graphs in a very large background graph. Our method builds upon the
graph-matching-matched-filter technique proposed in Sussman et al., with the
discovery of multiple diverse matchings being achieved by iteratively
penalizing a suitable node-pair similarity matrix in the matched filter
algorithm. In addition, we propose algorithmic speed-ups that greatly enhance
the scalability of our matched-filter approach. We present theoretical
justification of our methodology in the setting of correlated Erdos-Renyi
graphs, showing its ability to sequentially discover multiple templates under
mild model conditions. We additionally demonstrate our method's utility via
extensive experiments both using simulated models and real-world dataset,
include human brain connectomes and a large transactional knowledge base.
