Label propagation algorithm: a semi-synchronous approach

摘要

A recently introduced novel community detection strategy is based on a label propagation (LP) algorithm which uses the diffusion of information in the network to identify communities. Studies of LP algorithms 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 not granted in the first case, performances can be worst in the second case. In this paper, we present a semi-synchronous version of LP algorithms which aims to combine the advantages of both synchronous and asynchronous models. We prove that our models always converge to a stable labelling. 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 parallelisable and it is more stable than the asynchronous model, thanks to the fact that only a small amount of randomisation is used by our proposal.