  Community detection is a popular approach to understand the organization of
interactions in static networks. For that purpose, the Clique Percolation
Method (CPM), which involves the percolation of k-cliques, is a well-studied
technique that offers several advantages. Besides, studying interactions that
occur over time is useful in various contexts, which can be modeled by the link
stream formalism. The Dynamic Clique Percolation Method (DCPM) has been
proposed for extending CPM to temporal networks.
  However, existing implementations are unable to handle massive datasets. We
present a novel algorithm that adapts CPM to link streams, which has the
advantage that it allows us to speed up the computation time with respect to
the existing DCPM method. We evaluate it experimentally on real datasets and
show that it scales to massive link streams. For example, it allows to obtain a
complete set of communities in under twenty-five minutes for a dataset with
thirty million links, what the state of the art fails to achieve even after a
week of computation. We further show that our method provides communities
similar to DCPM, but slightly more aggregated. We exhibit the relevance of the
obtained communities in real world cases, and show that they provide
information on the importance of vertices in the link streams.
