 This paper proposes a new objective function called Generalized Modularity Density, Q underscore G. It is designed to address the issue of resolution limit, RL, which occurs when a network is too dense and cannot be properly represented by traditional modularity metrics. This paper shows that Q underscore G can be used to identify modular structure in real world and artificial networks that would otherwise be hidden due to the RL problem. Additionally, the paper demonstrates how the RL problem can be quantified using a benchmark test, and examines various modularity-like objective functions to show that Q underscore G performs best. This article was authored by Jiahao Guo, Pramish Singh, and Kevin E. Basler.