Communities play a crucial role to describe and analyse modern networks. However, the size of those networks has grown tremendously with the increase of computational power and data storage. While various methods have been developed to extract community structures, their computational cost or the difficulty to parallelize existing algorithms make partitioning real networks into communities a challenging problem. In this paper, we propose to alter an efficient algorithm, the Louvain method, such that communities are defined as the connected components of a tree-like assignment graph. Within this framework, we precisely describe the different steps of our algorithm and demonstrate its highly parallelizable nature. We then show that despite its simplicity, our algorithm has a partitioning quality similar to the original method on benchmark graphs and even outperforms other algorithms. We also show that, even on a single processor, our method is much faster and allows the analysis of very large networks.