  A recently introduced novel community detection strategy is based on a label
propagation algorithm (LPA) which uses the diffusion of information in the
network to identify communities. Studies of LPAs showed that the strategy is
effective in finding a good community structure. Label propagation step can be
performed in parallel on all nodes (synchronous model) or sequentially
(asynchronous model); both models present some drawback, e.g., algorithm
termination is nor granted in the first case, performances can be worst in the
second case. In this paper, we present a semi-synchronous version of LPA which
aims to combine the advantages of both synchronous and asynchronous models. We
prove that our models always converge to a stable labeling. Moreover, we
experimentally investigate the effectiveness of the proposed strategy comparing
its performance with the asynchronous model both in terms of quality,
efficiency and stability. Tests show that the proposed protocol does not harm
the quality of the partitioning. Moreover it is quite efficient; each
propagation step is extremely parallelizable and it is more stable than the
asynchronous model, thanks to the fact that only a small amount of
randomization is used by our proposal.
