The paper introduces a new algorithm called the Smart Local Moving (SLM) algorithm for modularity-based community detection in large networks. The SLM algorithm leverages a well-known local moving heuristic, which is also used by other algorithms like the popular Louvain algorithm. However, the SLM algorithm employs this heuristic in a more sophisticated manner, leading to higher modularity values compared to other algorithms, including the Louvain algorithm. The SLM algorithm is designed to handle networks with tens of millions of nodes and hundreds of millions of edges, making it computationally efficient for large-scale applications. The algorithm is also effective in small and medium-sized networks, achieving modularity values that are competitive with or even higher than those reported in the literature. The paper compares the SLM algorithm with the Louvain algorithm and its multilevel refinement version, showing that the SLM algorithm consistently outperforms them in terms of modularity, especially when a large number of iterations are performed. The SLM algorithm's performance is further evaluated on 13 small to medium-sized networks and six large networks, demonstrating its robustness and efficiency. The SLM algorithm's ability to produce high-quality community structures at a lower computational cost compared to existing algorithms makes it a valuable tool for large-scale network analysis.The paper introduces a new algorithm called the Smart Local Moving (SLM) algorithm for modularity-based community detection in large networks. The SLM algorithm leverages a well-known local moving heuristic, which is also used by other algorithms like the popular Louvain algorithm. However, the SLM algorithm employs this heuristic in a more sophisticated manner, leading to higher modularity values compared to other algorithms, including the Louvain algorithm. The SLM algorithm is designed to handle networks with tens of millions of nodes and hundreds of millions of edges, making it computationally efficient for large-scale applications. The algorithm is also effective in small and medium-sized networks, achieving modularity values that are competitive with or even higher than those reported in the literature. The paper compares the SLM algorithm with the Louvain algorithm and its multilevel refinement version, showing that the SLM algorithm consistently outperforms them in terms of modularity, especially when a large number of iterations are performed. The SLM algorithm's performance is further evaluated on 13 small to medium-sized networks and six large networks, demonstrating its robustness and efficiency. The SLM algorithm's ability to produce high-quality community structures at a lower computational cost compared to existing algorithms makes it a valuable tool for large-scale network analysis.