Modern applications increasingly demand real-time analysis of live, dynamic graphs at scale, yet community detection remains a performance bottleneck—particularly for Louvain-based methods, which are inherently batch-oriented and unsuitable for high-velocity updates. In this paper, we present a novel real-time and incremental Louvain algorithm, integrated into a unified graph database framework that leverages Storage-Compute Clustering (SCC) and High-Density Computing (HDC). Our approach enables sub-second to sub-minute runtimes on billion-scale graphs by dynamically pruning change scopes, reusing modularity deltas, and incorporating deep neighborhood expansion. We analyze trade-offs of prior incremental Louvain variants and position our system as an architecturally grounded, accuracy-preserving alternative. Extensive evaluations demonstrate that our framework not only delivers superior runtime performance but also maintains high modularity, making it suitable for real-world use cases such as financial fraud detection, social network monitoring, and infrastructure anomaly detection.