A novel collaborative effectiveness-driven team formation strategy in social networks

The rapid development of internet technology has heightened interest in identifying teams with high collaborative effectiveness in complex and diverse social networks. Earlier research primarily aimed to minimize team communication costs while ensuring that task-related skill requirements were fulfilled. However, this single-factor approach often leads to uneven workload distribution among team members, which can undermine overall performance. To this end, a multi-constraint optimization method was introduced that considers both communication cost minimization and workload balancing. This method sets an upper limit on the number of skills that can be assigned to any individual within a team. The proposed framework compares two main algorithms: genetic and greedy. Experimental results using the DBLP dataset highlighted the distinct strengths of each. The genetic algorithm (GA) outperformed in reducing communication costs and decreasing team size, whereas the greedy algorithm excelled in lowering the number of disconnected teams and achieving shorter runtime. The inclusion of additional constraints in the multi-constraint optimization framework increased communication costs, extended algorithm runtime and produced larger team sizes compared with the single-constraint model. Nevertheless, the proposed approach provides a flexible solution that can be adapted to different priorities, whether emphasizing strict task requirements or optimizing factors.