Starting from Edge-cut/Vertex-cut objective functions, this article systematically explains METIS-style multilevel partitioning and production implementation, with emphasis on how partitioning affects query latency and cross-machine traffic.
Subtitle / Abstract Community detection is not just “splitting a graph into a few groups.” In production, you must balance accuracy, interpretability, speed, and maintainability. Following the ACERS structure, this article breaks down two of the most common engineering choices: Louvain (modularity optimization) and Label Propagation (LPA). Estimated reading time: 12-16 minutes Tags: Community Detection, Louvain, Label Propagation, Graph Partitioning SEO keywords: community detection, Louvain, Label Propagation, modularity, graph partition Meta description: Engineering primer for community detection: principles, complexity, algorithm selection, and implementation templates for Louvain and LPA across group discovery, graph partitioning, and cold start. Target Audience Engineers working on social graphs, risk-control graphs, or recommender-system graph analytics Developers who want to move community detection from paper concepts into production workflows Practitioners modeling group structure for graph partitioning and cold-start scenarios Background / Motivation Community detection appears frequently in production: ...