A Distributed Approach to Community Detection in Large Social Networks Based on Label Propagation

Detection of overlapping communities in large complex social networks with intelligent agents, is an NP problem with great time complexity and large memory usage and no simultaneous online solution.  Proposing a novel distributed label propagation approach can help to decrease the searching time and reduce the memory space usage. This paper presents a scalable distributed overlapping community  detection approach based on the label propagation method by proposing a novel algorithm and three new metrics to expand scalability and improve modularity through agent-based implementation and good memory allocation in a multi-core architecture. The experimental results of large real datasets over the state-of-the-art SLPA approach show that the execution time speeds up by 900% and the modularity improves by 3% to 100% thus producing fast and accurate detection of overlapped communities.