A Memetic Algorithm for the Capacity-Constrained Community Detection Problem in Social Networks

Community detection in social networks plays a vital role in understanding structural influence and optimizing promotional strategies. This study proposes a capacity-constrained community detection problem (CCDP) model to identify the most influential community under practical limitations of promotion opportunities. Given the NP-hard nature of the problem, an efficient memetic algorithm is developed. Extensive experiments demonstrate its superiority over CPLEX and several heuristic and community detection approaches. Further analysis on convergence, parameter sensitivity, and crossover operator provides deeper insights into the algorithm’s performance.