LB-SAM: Local Beam Search With Simulated Annealing for Community Detection in Large-Scale Social Networks

With the rapid development of internet technologies and the increasing availability of large-scale data, the detection of community structures within complex networks has become a critical area of research. This paper introduces a novel community detection technique called LB-SAM (Local Beam Search with Simulated Annealing and Modularity), designed to efficiently uncover hidden community structures in large-scale social networks. LB-SAM integrates Local Beam Search (LBS) to explore the local network structure and Simulated Annealing (SA) to globally optimize modularity, enabling the detection of communities with intricate boundaries and strong internal connections. By focusing on influential nodes to form subgroups and recursively merging them based on modularity, LB-SAM provides superior scalability and robustness in both real-world and synthetic networks. Extensive experiments conducted on 12 real-world and 6 synthetic datasets demonstrate that LB-SAM consistently outperforms existing state-of-the-art algorithms, particularly in networks with unclear community structures, and scales effectively to billion-scale networks. The proposed method has wide-ranging applications in sociology, biology, marketing, and cybersecurity, offering valuable insights into the structure and dynamics of large social networks.