Parallelizing maximal clique and k-plex enumeration over graph data.

In a wide variety of emerging data-intensive applications, such as social network analysis, Web document clustering, entity resolution, and detection of consistently co-expressed genes in systems biology, the detection of dense subgraphs cliques and k-plex is an essential component. Unfortunately, these problems are NP-Complete and thus computationally intensive at scale — hence there is a need to come up with techniques for distributing the computation across multiple machines such that the computation, which is too time-consuming on a single machine, can be efficiently performed on a machine cluster given that it is large enough.