Complex network community discovery using fast local move iterated greedy algorithm

Community detection is crucial for understanding the structure and function of biological, social, and technological systems. This paper presents a novel algorithm, fast local move iterated greedy (FLMIG), which enhances the Louvain Prune heuristic using an iterated greedy (IG) framework to maximize modularity in non-overlapping communities. FLMIG combines efficient local optimization from the fast local move heuristic with iterative refinement through destruction and reconstruction phases. A key refinement step ensures that detected communities remain internally connected, addressing limitations of previous methods. The algorithm is scalable, parameter-light, and performs efficiently on large networks. Comparative evaluations against state-of-the-art methods, such as Leiden, iterated carousel greedy, and Louvain Prune algorithms, show that FLMIG delivers statistically comparable results with lower computational complexity. Extensive experiments on synthetic and real-world networks confirm FLMIG's ability to detect high-quality communities while maintaining robust performance across various network sizes, particularly improving modularity and execution time in large-scale networks.