Before revision:  Then ,  we show that this quantity has implications in three research areas within network science: community analysis, network robustness, and community deception.
After revision:  Then , or scale well, but not both. We propose an extremely scalable measure, modularity-impact, that is directly derived from Newman's modularity. Unlike other measures, modularity-impact is signed, providing additional information about each node's role: hub within its community, or bridge between communities. We analyze our results in a network robustness framework, as it is a generalized version of immunization under SIR models, which prior works have used for evaluation. We show that our measure out-performs previous measures for immunizing strategies in generated networks with strong community structure. Additionally, we show that this quantity has implications in three research areas within network science: community analysis, network robustness, and community deception.