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Community detection is a popular approach to understand the organization ofinteractions in static networks. For that purpose, the Clique PercolationMethod (CPM), which involves the percolation of k-cliques, is a well-studiedtechnique that offers several advantages. Besides, studying interactions thatoccur over time is useful in various contexts, which can be modeled by the linkstream formalism. The Dynamic Clique Percolation Method (DCPM) has beenproposed for extending CPM to temporal networks.  However, existing implementations are unable to handle massive datasets. Wepresent a novel algorithm that adapts CPM to link streams, which has theadvantage that it allows us to speed up the computation time with respect tothe existing DCPM method. We evaluate it experimentally on real datasets andshow that it scales to massive link streams. For example, it allows to obtain acomplete set of communities in under twenty-five minutes for a dataset withthirty million links, what the state of the art fails to achieve even after aweek of computation. We further show that our method provides communitiessimilar to DCPM, but slightly more aggregated. We exhibit the relevance of theobtained communities in real world cases, and show that they provideinformation on the importance of vertices in the link streams.

LSCPM: communities in massive real-world Link Streams by Clique Percolation Method